{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/conv3x3","entry":"conv3x3","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":1376,"n_papers_ran":1241,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":257,"n_samples_ran":126,"n_samples_fingerprinted":1,"n_places":1577,"n_places_pointer_only":444,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":84,"ran_fixture":0,"ran":42,"unverified":131},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2609.15503","paper":"/paper/arxiv-2609-15503","title":"SyntheticDoc: A Large Synthetic Dataset for Document Unwarping and Illumination Correction","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"tanguymagne/SyntheticDoc","path":"training/model.py","file_url":"https://github.com/tanguymagne/SyntheticDoc/blob/HEAD/training/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7719b3690410093e","mcp_get_code":{"code_sha256":"7719b3690410093e"}},{"arxiv_id":"2609.15400","paper":"/paper/arxiv-2609-15400","title":"BSC-Net: A Small-Branch-Sensitive Structural Continuity Network for Coronary Vessel Segmentation and Quantitative Angiographic Analysis","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"liwx-deeplearning/BSC-Net","path":"bscnet/model.py","file_url":"https://github.com/liwx-deeplearning/BSC-Net/blob/HEAD/bscnet/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6c78331c95fa908e","mcp_get_code":{"code_sha256":"6c78331c95fa908e"}},{"arxiv_id":"2609.02644","paper":"/paper/arxiv-2609-02644","title":"Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"RAIB-group/DQM-Face","path":"backbones/iresnet.py","file_url":"https://github.com/RAIB-group/DQM-Face/blob/HEAD/backbones/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2609.01141","paper":"/paper/arxiv-2609-01141","title":"Revisiting Face Recognition for Monozygotic Twins: The Celeb Twins Test Set","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"mzang20/CTTS","path":"model/iresnet.py","file_url":"https://github.com/mzang20/CTTS/blob/HEAD/model/iresnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8484c2714df844f4","mcp_get_code":{"code_sha256":"8484c2714df844f4"}},{"arxiv_id":"2608.30688","paper":"/paper/arxiv-2608-30688","title":"UFPR-PEs: A Brazilian Face Recognition Benchmark with Self-Declared Race/Color Labels","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"UFPR-IPASP-PR/UFPR-PEs","path":"face_selection/arcface_torch/backbones/iresnet.py","file_url":"https://github.com/UFPR-IPASP-PR/UFPR-PEs/blob/HEAD/face_selection/arcface_torch/backbones/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2608.23363","paper":"/paper/arxiv-2608-23363","title":"DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"vladhondru25/DF-MoE","path":"face_parsing/resnet.py","file_url":"https://github.com/vladhondru25/DF-MoE/blob/HEAD/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2608.11285","paper":"/paper/arxiv-2608-11285","title":"SegPAR: Class-Centric Decision-Based Sparse Attack for Semantic Segmentation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"KAU-QuantumAILab/SegPAR","path":"adv_models/resnet.py","file_url":"https://github.com/KAU-QuantumAILab/SegPAR/blob/HEAD/adv_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2608.00716","paper":"/paper/arxiv-2608-00716","title":"Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"ZhendongWang6/DIRE","path":"networks/resnet.py","file_url":"https://github.com/ZhendongWang6/DIRE/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2606.19184","paper":"/paper/arxiv-2606-19184","title":"When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"megvii-research/CADDM","path":"backbones/resnet.py","file_url":"https://github.com/megvii-research/CADDM/blob/HEAD/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"vimar-gu/MinimaxDiffusion","path":"train_models/resnet.py","file_url":"https://github.com/vimar-gu/MinimaxDiffusion/blob/HEAD/train_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2606.13818","paper":"/paper/arxiv-2606-13818","title":"PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Ludvins/FixedMeanGaussianProcesses","path":"bayesipy/utils/pretrained_models/resnet.py","file_url":"https://github.com/Ludvins/FixedMeanGaussianProcesses/blob/HEAD/bayesipy/utils/pretrained_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2606.10309","paper":"/paper/arxiv-2606-10309","title":"Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"dahyedahye/dear","path":"dear/detector/corvi_mask_gated_detector.py","file_url":"https://github.com/dahyedahye/dear/blob/HEAD/dear/detector/corvi_mask_gated_detector.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"67c9ab8e625b4e5f","mcp_get_code":{"code_sha256":"67c9ab8e625b4e5f"}},{"arxiv_id":"2606.00738","paper":"/paper/arxiv-2606-00738","title":"SORA: Free Second-Order Attacks in Fast Adversarial Training","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"val-iisc/NuAT","path":"CIFAR10/WideResNet.py","file_url":"https://github.com/val-iisc/NuAT/blob/HEAD/CIFAR10/WideResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2606.00738","paper":"/paper/arxiv-2606-00738","title":"SORA: Free Second-Order Attacks in Fast Adversarial Training","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"val-iisc/NuAT","path":"CIFAR10/ResNet.py","file_url":"https://github.com/val-iisc/NuAT/blob/HEAD/CIFAR10/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2605.20735","paper":"/paper/arxiv-2605-20735","title":"Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"CVRL/OpenSourceIrisRecognition","path":"methods/ArcIris/Python/modules/network.py","file_url":"https://github.com/CVRL/OpenSourceIrisRecognition/blob/HEAD/methods/ArcIris/Python/modules/network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2605.17839","paper":"/paper/arxiv-2605-17839","title":"Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"phan-tho/Bilevel-balancing-kd","path":"model/resnet.py","file_url":"https://github.com/phan-tho/Bilevel-balancing-kd/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2605.14252","paper":"/paper/arxiv-2605-14252","title":"Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"KaiSUN1/SeAl","path":"model/ResNet_ANN.py","file_url":"https://github.com/KaiSUN1/SeAl/blob/HEAD/model/ResNet_ANN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2605.14252","paper":"/paper/arxiv-2605-14252","title":"Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"KaiSUN1/SeAl","path":"model/preact_resnet.py","file_url":"https://github.com/KaiSUN1/SeAl/blob/HEAD/model/preact_resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ba22fada8b666dd3","mcp_get_code":{"code_sha256":"ba22fada8b666dd3"}},{"arxiv_id":"2605.14252","paper":"/paper/arxiv-2605-14252","title":"Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"KaiSUN1/SeAl","path":"model/resnet.py","file_url":"https://github.com/KaiSUN1/SeAl/blob/HEAD/model/resnet.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cddb8eda1eb5217a","mcp_get_code":{"code_sha256":"cddb8eda1eb5217a"}},{"arxiv_id":"2605.13475","paper":"/paper/arxiv-2605-13475","title":"FedHPro: Federated Hyper-Prototype Learning via Gradient Matching","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"mala-lab/FedHPro","path":"main_run.py","file_url":"https://github.com/mala-lab/FedHPro/blob/HEAD/main_run.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"334d6dae5b67e789","mcp_get_code":{"code_sha256":"334d6dae5b67e789"}},{"arxiv_id":"2605.10047","paper":"/paper/arxiv-2605-10047","title":"Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"tongzixin716716/Inverse-Loss-Reweighting","path":"models/resnext.py","file_url":"https://github.com/tongzixin716716/Inverse-Loss-Reweighting/blob/HEAD/models/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2604.07166","paper":"/paper/arxiv-2604-07166","title":"DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ThomasNorr/Qpm","path":"architectures/resnet.py","file_url":"https://github.com/ThomasNorr/Qpm/blob/HEAD/architectures/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2603.26671","paper":"/paper/arxiv-2603-26671","title":"Mitigating Forgetting in Continual Learning with Selective Gradient Projection","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"anixa-s/sfao","path":"continual_learning/models/reduced_resnet18.py","file_url":"https://github.com/anixa-s/sfao/blob/HEAD/continual_learning/models/reduced_resnet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d2cd695adf5c4268","mcp_get_code":{"code_sha256":"d2cd695adf5c4268"}},{"arxiv_id":"2603.24209","paper":"/paper/arxiv-2603-24209","title":"HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfer","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"danny0628/HEART-PFL","path":"models/resnet_bn_utils.py","file_url":"https://github.com/danny0628/HEART-PFL/blob/HEAD/models/resnet_bn_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"54ef4a4d895678ee","mcp_get_code":{"code_sha256":"54ef4a4d895678ee"}},{"arxiv_id":"2603.18865","paper":"/paper/arxiv-2603-18865","title":"RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"UNIC-Lab/RadioDiff-FS","path":"denoising_diffusion_pytorch/resnet.py","file_url":"https://github.com/UNIC-Lab/RadioDiff-FS/blob/HEAD/denoising_diffusion_pytorch/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2603.14956","paper":"/paper/arxiv-2603-14956","title":"SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"rtao499/SFedHIFI","path":"model/spiking_resnet.py","file_url":"https://github.com/rtao499/SFedHIFI/blob/HEAD/model/spiking_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6e1a78205fb3921","mcp_get_code":{"code_sha256":"d6e1a78205fb3921"}},{"arxiv_id":"2603.14238","paper":"/paper/arxiv-2603-14238","title":"Domain-Skewed Federated Learning with Feature Decoupling and Calibration","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"mala-lab/F2DC","path":"backbone/ResNet.py","file_url":"https://github.com/mala-lab/F2DC/blob/HEAD/backbone/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2603.08426","paper":"/paper/arxiv-2603-08426","title":"Grow, Assess, Compress: Adaptive Backbone Scaling for Memory-Efficient Class Incremental Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"ai-digilab/GRACE","path":"models/grace.py","file_url":"https://github.com/ai-digilab/GRACE/blob/HEAD/models/grace.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0fae381fb2fd54e6","mcp_get_code":{"code_sha256":"0fae381fb2fd54e6"}},{"arxiv_id":"2603.02200","paper":"/paper/arxiv-2603-02200","title":"Adaptive Confidence Regularization for Multimodal Failure Detection","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"mona4399/ACR","path":"EPIC-rgb-flow/VGGSound/models/resnet.py","file_url":"https://github.com/mona4399/ACR/blob/HEAD/EPIC-rgb-flow/VGGSound/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2602.17947","paper":"/paper/arxiv-2602-17947","title":"Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"kjunelee/MetaOptNet","path":"models/ResNet12_embedding.py","file_url":"https://github.com/kjunelee/MetaOptNet/blob/HEAD/models/ResNet12_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2602.04266","paper":"/paper/arxiv-2602-04266","title":"Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"PKUDigitalHealth/PiLA-PPG","path":"src/models.py","file_url":"https://github.com/PKUDigitalHealth/PiLA-PPG/blob/HEAD/src/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f333a7d77f06bc38","mcp_get_code":{"code_sha256":"f333a7d77f06bc38"}},{"arxiv_id":"2602.03824","paper":"/paper/arxiv-2602-03824","title":"Quantifying Avian Morphological Evolution through Deep Representation Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"sun-jiao/MetaFGNet","path":"MetaFGNet_with_Sample_Selection/models/resnet.py","file_url":"https://github.com/sun-jiao/MetaFGNet/blob/HEAD/MetaFGNet_with_Sample_Selection/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2602.03157","paper":"/paper/arxiv-2602-03157","title":"Human-in-the-loop adaptation in group activity feature learning for team sports video retrieval","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"chihina/GAFL-FINE-CVIU","path":"sub_model.py","file_url":"https://github.com/chihina/GAFL-FINE-CVIU/blob/HEAD/sub_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2602.03024","paper":"/paper/arxiv-2602-03024","title":"Consistency Deep Equilibrium Models","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"landrarwolf/CDEQ","path":"CDEQ-src/lib/layer_utils.py","file_url":"https://github.com/landrarwolf/CDEQ/blob/HEAD/CDEQ-src/lib/layer_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"2602.02060","paper":"/paper/arxiv-2602-02060","title":"FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"AndreyGuzhov/AudioCLIP","path":"model/esresnet/base.py","file_url":"https://github.com/AndreyGuzhov/AudioCLIP/blob/HEAD/model/esresnet/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"efd572a4ee36267c","mcp_get_code":{"code_sha256":"efd572a4ee36267c"}},{"arxiv_id":"2602.01219","paper":"/paper/arxiv-2602-01219","title":"Mixture-of-Top-k Attention: Efficient Attention via Scalable Fast Weights","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"QishuaiWen/MiTA","path":"MiTA-DeiT/patchconvnet_models.py","file_url":"https://github.com/QishuaiWen/MiTA/blob/HEAD/MiTA-DeiT/patchconvnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba6aa5f07daca9cd","mcp_get_code":{"code_sha256":"ba6aa5f07daca9cd"}},{"arxiv_id":"2601.07377","paper":"/paper/arxiv-2601-07377","title":"Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"xujiaommcome/DiCo","path":"code/networks/resnet.py","file_url":"https://github.com/xujiaommcome/DiCo/blob/HEAD/code/networks/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e14a56fb15bfea1f","mcp_get_code":{"code_sha256":"e14a56fb15bfea1f"}},{"arxiv_id":"2601.03805","paper":"/paper/arxiv-2601-03805","title":"Detecting Semantic Backdoors in a Mystery Shopping Scenario","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"szegedai/SemanticBackdoorDetection","path":"models/resnetmod_ulp.py","file_url":"https://github.com/szegedai/SemanticBackdoorDetection/blob/HEAD/models/resnetmod_ulp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2511.17399","paper":"/paper/arxiv-2511-17399","title":"Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"changwk1001/stable-coreset","path":"models/resnet50_noise.py","file_url":"https://github.com/changwk1001/stable-coreset/blob/HEAD/models/resnet50_noise.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2511.17399","paper":"/paper/arxiv-2511-17399","title":"Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"decile-team/cords","path":"cords/utils/models/cnn13.py","file_url":"https://github.com/decile-team/cords/blob/HEAD/cords/utils/models/cnn13.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4cc0545d245c414","mcp_get_code":{"code_sha256":"c4cc0545d245c414"}},{"arxiv_id":"2511.10859","paper":"/paper/arxiv-2511-10859","title":"Private Zeroth-Order Optimization with Public Data","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"xuchengong/pazo","path":"experiments/pazo-p.py","file_url":"https://github.com/xuchengong/pazo/blob/HEAD/experiments/pazo-p.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3cf6280fecab1854","mcp_get_code":{"code_sha256":"3cf6280fecab1854"}},{"arxiv_id":"2511.05064","paper":"/paper/arxiv-2511-05064","title":"Order-Level Attention Similarity Across Language Models: A Latent Commonality","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"jinglin-liang/OLAS","path":"convs/resnet.py","file_url":"https://github.com/jinglin-liang/OLAS/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2511.05064","paper":"/paper/arxiv-2511-05064","title":"Order-Level Attention Similarity Across Language Models: A Latent Commonality","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"jinglin-liang/OLAS","path":"convs/resnet_cbam.py","file_url":"https://github.com/jinglin-liang/OLAS/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2511.05064","paper":"/paper/arxiv-2511-05064","title":"Order-Level Attention Similarity Across Language Models: A Latent Commonality","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"jinglin-liang/OLAS","path":"convs/modified_represnet.py","file_url":"https://github.com/jinglin-liang/OLAS/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2511.02667","paper":"/paper/arxiv-2511-02667","title":"Scalable Evaluation and Neural Models for Compositional Generalization","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"IBM/scalable-compositional-generalization","path":"visgen/models/resnet.py","file_url":"https://github.com/IBM/scalable-compositional-generalization/blob/HEAD/visgen/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7d5d6f956d3b9d74","mcp_get_code":{"code_sha256":"7d5d6f956d3b9d74"}},{"arxiv_id":"2510.16446","paper":"/paper/arxiv-2510-16446","title":"VIPAMIN: Visual Prompt Initialization via Embedding Selection and Subspace Expansion","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"iamjaekyun/vipamin","path":"src/models/vit_prompt/vit_exp_self.py","file_url":"https://github.com/iamjaekyun/vipamin/blob/HEAD/src/models/vit_prompt/vit_exp_self.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"670b335809d02529","mcp_get_code":{"code_sha256":"670b335809d02529"}},{"arxiv_id":"2510.14741","paper":"/paper/arxiv-2510-14741","title":"DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"perceivelab/dexter","path":"dexter/models/resnet.py","file_url":"https://github.com/perceivelab/dexter/blob/HEAD/dexter/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4355a2ab33ffcc4","mcp_get_code":{"code_sha256":"a4355a2ab33ffcc4"}},{"arxiv_id":"2510.12479","paper":"/paper/arxiv-2510-12479","title":"MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"NYCU-MAPL/MHLVC","path":"compressai/RIFE/FFNet.py","file_url":"https://github.com/NYCU-MAPL/MHLVC/blob/HEAD/compressai/RIFE/FFNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fce10771ab1d00db","mcp_get_code":{"code_sha256":"fce10771ab1d00db"}},{"arxiv_id":"2510.01278","paper":"/paper/arxiv-2510-01278","title":"Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Hengwei-Zhao96/NcPU","path":"utils_model/NcPU.py","file_url":"https://github.com/Hengwei-Zhao96/NcPU/blob/HEAD/utils_model/NcPU.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2509.26045","paper":"/paper/arxiv-2509-26045","title":"Scaling Up Temporal Domain Generalization via Temporal Experts Averaging","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"zxcvfd13502/TEA","path":"networks/resnet_gi.py","file_url":"https://github.com/zxcvfd13502/TEA/blob/HEAD/networks/resnet_gi.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2509.21387","paper":"/paper/arxiv-2509-21387","title":"Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"sanishsuwal7/Neurips-CogInterp","path":"code/resnet/resnet_18.py","file_url":"https://github.com/sanishsuwal7/Neurips-CogInterp/blob/HEAD/code/resnet/resnet_18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47076f7bdc0281ad","mcp_get_code":{"code_sha256":"47076f7bdc0281ad"}},{"arxiv_id":"2509.19674","paper":"/paper/arxiv-2509-19674","title":"C²Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"zhoujiahuan1991/NeurIPS2025-C2Prompt","path":"ResNet.py","file_url":"https://github.com/zhoujiahuan1991/NeurIPS2025-C2Prompt/blob/HEAD/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2508.21695","paper":"/paper/arxiv-2508-21695","title":"Activation Subspaces for Out-of-Distribution Detection","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"visinf/actsub","path":"actsub_standard/models/ood_resnet.py","file_url":"https://github.com/visinf/actsub/blob/HEAD/actsub_standard/models/ood_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2508.17817","paper":"/paper/arxiv-2508-17817","title":"TemCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"Meiqi-Gong/TemCoCo","path":"archs/net_basics.py","file_url":"https://github.com/Meiqi-Gong/TemCoCo/blob/HEAD/archs/net_basics.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2508.02288","paper":"/paper/arxiv-2508-02288","title":"Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"mickeykang16/Ev-Stereo3D","path":"liga/models/backbones_3d_stereo/basicblock.py","file_url":"https://github.com/mickeykang16/Ev-Stereo3D/blob/HEAD/liga/models/backbones_3d_stereo/basicblock.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2507.19125","paper":null,"title":"arXiv:2507.19125","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"lyq133/LIC-HPCM","path":"src/layers/conv.py","file_url":"https://github.com/lyq133/LIC-HPCM/blob/HEAD/src/layers/conv.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4550840ab238e2c8","mcp_get_code":{"code_sha256":"4550840ab238e2c8"}},{"arxiv_id":"2507.06482","paper":null,"title":"arXiv:2507.06482","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"hwang52/FedDifRC","path":"model/resnet_base.py","file_url":"https://github.com/hwang52/FedDifRC/blob/HEAD/model/resnet_base.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2507.06482","paper":null,"title":"arXiv:2507.06482","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"hwang52/FedDifRC","path":"model/resnet_cnn.py","file_url":"https://github.com/hwang52/FedDifRC/blob/HEAD/model/resnet_cnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2507.01630","paper":null,"title":"arXiv:2507.01630","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"YuxiaoWang-AI/P3HOT","path":"hot/models/hrnet.py","file_url":"https://github.com/YuxiaoWang-AI/P3HOT/blob/HEAD/hot/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2506.23151","paper":"/paper/memfof-high-resolution-training-for-memory","title":"MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation","date":"2025-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msu-video-group/memfof","path":"memfof/model.py","file_url":"https://github.com/msu-video-group/memfof/blob/HEAD/memfof/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"7599edadbb4f2bdc","mcp_get_code":{"code_sha256":"7599edadbb4f2bdc"}},{"arxiv_id":"2506.20548","paper":"/paper/pay-less-attention-to-deceptive-artifacts","title":"Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks","date":"2025-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manyilee/plada","path":"networks/resnet.py","file_url":"https://github.com/manyilee/plada/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2506.20548","paper":"/paper/pay-less-attention-to-deceptive-artifacts","title":"Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks","date":"2025-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manyilee/plada","path":"models/resnet.py","file_url":"https://github.com/manyilee/plada/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2506.02493","paper":"/paper/towards-in-the-wild-3d-plane-reconstruction-1","title":"Towards In-the-wild 3D Plane Reconstruction from a Single Image","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jcliu0428/ZeroPlane","path":"ZeroPlane/modeling/backbone/hrnet.py","file_url":"https://github.com/jcliu0428/ZeroPlane/blob/HEAD/ZeroPlane/modeling/backbone/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2505.24254","paper":"/paper/rethinking-continual-learning-with","title":"Rethinking Continual Learning with Progressive Neural Collapse","date":"2025-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Continue-Edge-AI-Lab/ProNC","path":"backbone/ResNetBottleneck.py","file_url":"https://github.com/Continue-Edge-AI-Lab/ProNC/blob/HEAD/backbone/ResNetBottleneck.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2505.24254","paper":"/paper/rethinking-continual-learning-with","title":"Rethinking Continual Learning with Progressive Neural Collapse","date":"2025-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Continue-Edge-AI-Lab/ProNC","path":"backbone/ResNetBlock.py","file_url":"https://github.com/Continue-Edge-AI-Lab/ProNC/blob/HEAD/backbone/ResNetBlock.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25e04c3b7a8cc075","mcp_get_code":{"code_sha256":"25e04c3b7a8cc075"}},{"arxiv_id":"2505.19813","paper":"/paper/golf-nrt-integrating-global-context-and-local","title":"GoLF-NRT: Integrating Global Context and Local Geometry for Few-Shot View Synthesis","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KLMAV-CUC/GoLF-NRT","path":"golf/model.py","file_url":"https://github.com/KLMAV-CUC/GoLF-NRT/blob/HEAD/golf/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2505.14239","paper":"/paper/decoupling-classifier-for-boosting-few-shot-1","title":"Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gaobb/DCFS","path":"dcfs/evaluation/archs/resnet.py","file_url":"https://github.com/gaobb/DCFS/blob/HEAD/dcfs/evaluation/archs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2505.12335","paper":"/paper/is-artificial-intelligence-generated-image","title":"Is Artificial Intelligence Generated Image Detection a Solved Problem?","date":"2025-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2505.05522","paper":"/paper/continuous-thought-machines","title":"Continuous Thought Machines","date":"2025-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huyvnphan/PyTorch_CIFAR10","path":"cifar10_models/resnet.py","file_url":"https://github.com/huyvnphan/PyTorch_CIFAR10/blob/HEAD/cifar10_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2505.02604","paper":"/paper/low-loss-space-in-neural-networks-is","title":"Low-Loss Space in Neural Networks is Continuous and Fully Connected","date":"2025-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fdraxler/PyTorch-AutoNEB","path":"torch_autoneb/models/resnet.py","file_url":"https://github.com/fdraxler/PyTorch-AutoNEB/blob/HEAD/torch_autoneb/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bde80d96c1e287d8","mcp_get_code":{"code_sha256":"bde80d96c1e287d8"}},{"arxiv_id":"2503.18258","paper":"/paper/severing-spurious-correlations-with-data","title":"Severing Spurious Correlations with Data Pruning","date":"2025-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2503.13828","paper":"/paper/scale-aware-contrastive-reverse-distillation","title":"Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"medaitech/scrd4ad","path":"models/de_resnet.py","file_url":"https://github.com/medaitech/scrd4ad/blob/HEAD/models/de_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2503.13828","paper":"/paper/scale-aware-contrastive-reverse-distillation","title":"Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"9ec14cd0fc7f9260","mcp_get_code":{"code_sha256":"9ec14cd0fc7f9260"}},{"arxiv_id":"2503.10625","paper":"/paper/lhm-large-animatable-human-reconstruction","title":"LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds","date":"2025-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aigc3d/LHM","path":"LHM/models/arcface_utils.py","file_url":"https://github.com/aigc3d/LHM/blob/HEAD/LHM/models/arcface_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2c887ef60cf0fea5","mcp_get_code":{"code_sha256":"2c887ef60cf0fea5"}},{"arxiv_id":"2503.09411","paper":"/paper/benefits-of-learning-rate-annealing-for","title":"Benefits of Learning Rate Annealing for Tuning-Robustness in Stochastic Optimization","date":"2025-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bmsookim/wide-resnet.pytorch","path":"networks/wide_resnet.py","file_url":"https://github.com/bmsookim/wide-resnet.pytorch/blob/HEAD/networks/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2503.02424","paper":"/paper/exploring-intrinsic-normal-prototypes-within","title":"Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection","date":"2025-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"septmars/DL","path":"ReContrast/de_resnet.py","file_url":"https://github.com/septmars/DL/blob/HEAD/ReContrast/de_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2501.10945","paper":"/paper/gradient-based-multi-objective-deep-learning","title":"Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond","date":"2025-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"median-research-group/libmtl","path":"LibMTL/model/resnet.py","file_url":"https://github.com/median-research-group/libmtl/blob/HEAD/LibMTL/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2501.01790","paper":"/paper/ingredients-blending-custom-photos-with-video","title":"Ingredients: Blending Custom Photos with Video Diffusion Transformers","date":"2025-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feizc/ingredients","path":"metric/curricularface/model_resnet.py","file_url":"https://github.com/feizc/ingredients/blob/HEAD/metric/curricularface/model_resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6ccc5338f47a440c","mcp_get_code":{"code_sha256":"6ccc5338f47a440c"}},{"arxiv_id":"2412.09441","paper":"/paper/mos-model-surgery-for-pre-trained-model-based","title":"MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning","date":"2024-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sun-hailong/lamda-pilot","path":"backbone/resnet.py","file_url":"https://github.com/sun-hailong/lamda-pilot/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2412.06209","paper":"/paper/sound2vision-generating-diverse-visuals-from","title":"Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"postech-ami/Sound2Scene","path":"models/resnet.py","file_url":"https://github.com/postech-ami/Sound2Scene/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2412.05823","paper":"/paper/dapperfl-domain-adaptive-federated-learning","title":"DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices","date":"2024-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jyzgh/DapperFL","path":"fedml_api/standalone/domain_generalization/backbone/ResNet.py","file_url":"https://github.com/jyzgh/DapperFL/blob/HEAD/fedml_api/standalone/domain_generalization/backbone/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2412.02527","paper":"/paper/the-multimodal-universe-enabling-large-scale","title":"The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"multimodaluniverse/multimodaluniverse","path":"experimental_benchmark/galaxy_properties/modules.py","file_url":"https://github.com/multimodaluniverse/multimodaluniverse/blob/HEAD/experimental_benchmark/galaxy_properties/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db485754880e0747","mcp_get_code":{"code_sha256":"db485754880e0747"}},{"arxiv_id":"2411.13918","paper":"/paper/quantization-without-tears","title":"Quantization without Tears","date":"2024-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wujx2001/QwT","path":"QwT-cls-RepQ-ViT/utils/resnet.py","file_url":"https://github.com/wujx2001/QwT/blob/HEAD/QwT-cls-RepQ-ViT/utils/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2411.13127","paper":"/paper/adapting-vision-foundation-models-for-robust","title":"Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images","date":"2024-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierjiezou/cloud-adapter","path":"hugging_face/cloud_adapter/cdnetv1.py","file_url":"https://github.com/xavierjiezou/cloud-adapter/blob/HEAD/hugging_face/cloud_adapter/cdnetv1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2411.13127","paper":"/paper/adapting-vision-foundation-models-for-robust","title":"Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images","date":"2024-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierjiezou/cloud-adapter","path":"hugging_face/cloud_adapter/cdnetv2.py","file_url":"https://github.com/xavierjiezou/cloud-adapter/blob/HEAD/hugging_face/cloud_adapter/cdnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2411.12615","paper":"/paper/a-multimodal-approach-combining-structural","title":"A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation","date":"2024-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangjiaqidig/WSSS-AGM","path":"anomaly_guided/alternatives_network/baselines.py","file_url":"https://github.com/yangjiaqidig/WSSS-AGM/blob/HEAD/anomaly_guided/alternatives_network/baselines.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2411.11924","paper":"/paper/dataset-distillers-are-good-label-denoisers","title":"Dataset Distillers Are Good Label Denoisers In the Wild","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kciiiman/dd_lnl","path":"DANCE+ours/models/resnet.py","file_url":"https://github.com/kciiiman/dd_lnl/blob/HEAD/DANCE%2Bours/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2411.09558","paper":"/paper/adaptive-deviation-learning-for-visual","title":"Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination","date":"2024-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anindyasdas/adl4vad","path":"modeling/networks/resnet18.py","file_url":"https://github.com/anindyasdas/adl4vad/blob/HEAD/modeling/networks/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"2411.02853","paper":"/paper/adopt-modified-adam-can-converge-with-any-b-2","title":"ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UiPath/torchvision","path":"torchvision/models/resnet.py","file_url":"https://github.com/UiPath/torchvision/blob/HEAD/torchvision/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2411.02467","paper":"/paper/towards-harmless-rawlsian-fairness-regardless","title":"Towards Harmless Rawlsian Fairness Regardless of Demographic Prior","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wxqpxw/VFair","path":"resnet.py","file_url":"https://github.com/wxqpxw/VFair/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2411.01981","paper":"/paper/typicalness-aware-learning-for-failure","title":"Typicalness-Aware Learning for Failure Detection","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyijungoon/TAL","path":"model/resnet.py","file_url":"https://github.com/liuyijungoon/TAL/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2411.01833","paper":"/paper/owmatch-conditional-self-labeling-with","title":"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"niusj03/OwMatch","path":"models/resnet.py","file_url":"https://github.com/niusj03/OwMatch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2411.01225","paper":"/paper/rle-a-unified-perspective-of-data","title":"RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification","date":"2024-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stone96123/RLE","path":"resnet.py","file_url":"https://github.com/stone96123/RLE/blob/HEAD/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"48468b2c75aae583","mcp_get_code":{"code_sha256":"48468b2c75aae583"}},{"arxiv_id":"2411.00899","paper":"/paper/certified-robustness-for-deep-equilibrium-1","title":"Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WeizhiGao/Serialized-Randomized-Smoothing","path":"SRS/code/archs/cifar_resnet.py","file_url":"https://github.com/WeizhiGao/Serialized-Randomized-Smoothing/blob/HEAD/SRS/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2411.00899","paper":"/paper/certified-robustness-for-deep-equilibrium-1","title":"Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WeizhiGao/Serialized-Randomized-Smoothing","path":"DEQ/lib/layer_utils.py","file_url":"https://github.com/WeizhiGao/Serialized-Randomized-Smoothing/blob/HEAD/DEQ/lib/layer_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"2411.00329","paper":"/paper/personalized-federated-learning-via-feature","title":"Personalized Federated Learning via Feature Distribution Adaptation","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cj-mclaughlin/pFedFDA","path":"models/resnet_gn.py","file_url":"https://github.com/cj-mclaughlin/pFedFDA/blob/HEAD/models/resnet_gn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2410.24037","paper":"/paper/tpc-test-time-procrustes-calibration-for","title":"TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbstjswo505/TPC","path":"TPC/densepose/modeling/hrnet.py","file_url":"https://github.com/dbstjswo505/TPC/blob/HEAD/TPC/densepose/modeling/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2410.23495","paper":"/paper/dash-warm-starting-neural-network-training-in","title":"DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baekrok/DASH-Direction-Aware-SHrinking","path":"models/ResNet.py","file_url":"https://github.com/baekrok/DASH-Direction-Aware-SHrinking/blob/HEAD/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2410.18472","paper":"/paper/what-if-the-input-is-expanded-in-ood","title":"What If the Input is Expanded in OOD Detection?","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/CoVer","path":"DNNs/resnet_dice.py","file_url":"https://github.com/tmlr-group/CoVer/blob/HEAD/DNNs/resnet_dice.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2410.18472","paper":"/paper/what-if-the-input-is-expanded-in-ood","title":"What If the Input is Expanded in OOD Detection?","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/CoVer","path":"DNNs/resnet_ash.py","file_url":"https://github.com/tmlr-group/CoVer/blob/HEAD/DNNs/resnet_ash.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2410.17020","paper":"/paper/lfme-a-simple-framework-for-learning-from","title":"LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization","date":"2024-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangchen527/LFME","path":"semantic_segmentation/network/Resnet.py","file_url":"https://github.com/liangchen527/LFME/blob/HEAD/semantic_segmentation/network/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2410.12671","paper":"/paper/new-paradigm-of-adversarial-training-breaking","title":"New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy Classes","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FlaAI/DUCAT","path":"models/resnet.py","file_url":"https://github.com/FlaAI/DUCAT/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2410.11582","paper":"/paper/on-the-fly-modulation-for-balanced-multimodal","title":"On-the-fly Modulation for Balanced Multimodal Learning","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gewu-lab/bml_tpami2024","path":"code/models/Resnet_18.py","file_url":"https://github.com/gewu-lab/bml_tpami2024/blob/HEAD/code/models/Resnet_18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2410.11559","paper":"/paper/why-go-full-elevating-federated-learning","title":"Why Go Full? Elevating Federated Learning Through Partial Network Updates","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FLAIR-Community/Fling","path":"fling/model/resnet.py","file_url":"https://github.com/FLAIR-Community/Fling/blob/HEAD/fling/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2410.11488","paper":"/paper/advancing-training-efficiency-of-deep-spiking","title":"Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tab-ct/rate-based-backpropagation","path":"model/resnet.py","file_url":"https://github.com/Tab-ct/rate-based-backpropagation/blob/HEAD/model/resnet.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cddb8eda1eb5217a","mcp_get_code":{"code_sha256":"cddb8eda1eb5217a"}},{"arxiv_id":"2410.11397","paper":"/paper/foogd-federated-collaboration-for-both-out-of","title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XeniaLLL/FOOGD-main","path":"src/models/resnet.py","file_url":"https://github.com/XeniaLLL/FOOGD-main/blob/HEAD/src/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e98e1d315e2ea27","mcp_get_code":{"code_sha256":"7e98e1d315e2ea27"}},{"arxiv_id":"2410.10122","paper":"/paper/musetalk-real-time-high-quality-lip","title":"MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmelyralab/musetalk","path":"musetalk/loss/resnet.py","file_url":"https://github.com/tmelyralab/musetalk/blob/HEAD/musetalk/loss/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2410.07838","paper":"/paper/minorityprompt-text-to-minority-image","title":"Minority-Focused Text-to-Image Generation via Prompt Optimization","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anonymous5293/minorityprompt","path":"utils/clf_models.py","file_url":"https://github.com/anonymous5293/minorityprompt/blob/HEAD/utils/clf_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2410.07679","paper":"/paper/relational-diffusion-distillation-for","title":"Relational Diffusion Distillation for Efficient Image Generation","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cantbebetter2/rdd","path":"classifier/resnet.py","file_url":"https://github.com/cantbebetter2/rdd/blob/HEAD/classifier/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2410.07679","paper":"/paper/relational-diffusion-distillation-for","title":"Relational Diffusion Distillation for Efficient Image Generation","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cantbebetter2/rdd","path":"classifier/imagenet_resnet.py","file_url":"https://github.com/cantbebetter2/rdd/blob/HEAD/classifier/imagenet_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2410.07507","paper":"/paper/thought2text-text-generation-from-eeg-signal","title":"Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhijitmishra/Thought2Text","path":"channelnet/layers.py","file_url":"https://github.com/abhijitmishra/Thought2Text/blob/HEAD/channelnet/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2410.07286","paper":"/paper/benchmarking-data-heterogeneity-evaluation","title":"Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoni-61/dh-benchmark","path":"resnetcifar.py","file_url":"https://github.com/xiaoni-61/dh-benchmark/blob/HEAD/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2410.06645","paper":"/paper/continual-learning-in-the-frequency-domain","title":"Continual Learning in the Frequency Domain","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EMLS-ICTCAS/CLFD","path":"backbone/ResNet50.py","file_url":"https://github.com/EMLS-ICTCAS/CLFD/blob/HEAD/backbone/ResNet50.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2410.06645","paper":"/paper/continual-learning-in-the-frequency-domain","title":"Continual Learning in the Frequency Domain","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EMLS-ICTCAS/CLFD","path":"backbone/ResNet18.py","file_url":"https://github.com/EMLS-ICTCAS/CLFD/blob/HEAD/backbone/ResNet18.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25e04c3b7a8cc075","mcp_get_code":{"code_sha256":"25e04c3b7a8cc075"}},{"arxiv_id":"2410.06645","paper":"/paper/continual-learning-in-the-frequency-domain","title":"Continual Learning in the Frequency Domain","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EMLS-ICTCAS/CLFD","path":"backbone/DWTNet.py","file_url":"https://github.com/EMLS-ICTCAS/CLFD/blob/HEAD/backbone/DWTNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2410.02675","paper":"/paper/fan-fourier-analysis-networks","title":"FAN: Fourier Analysis Networks","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/FAN","path":"detection/models/fan.py","file_url":"https://github.com/NVlabs/FAN/blob/HEAD/detection/models/fan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"43d01a8da4163422","mcp_get_code":{"code_sha256":"43d01a8da4163422"}},{"arxiv_id":"2410.00418","paper":"/paper/posterior-mean-rectified-flow-towards-minimum","title":"Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ohayonguy/PMRF","path":"evaluation/awing_arch.py","file_url":"https://github.com/ohayonguy/PMRF/blob/HEAD/evaluation/awing_arch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cd28335574411d48","mcp_get_code":{"code_sha256":"cd28335574411d48"}},{"arxiv_id":"2409.19720","paper":"/paper/fast-a-dual-tier-few-shot-learning-paradigm","title":"FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification","date":"2024-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fukexue/FAST","path":"models/resnetv1.py","file_url":"https://github.com/fukexue/FAST/blob/HEAD/models/resnetv1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.19660","paper":"/paper/all-in-one-image-coding-for-joint-human","title":"All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation","date":"2024-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NJUVISION/MPA","path":"seg_model/resnet.py","file_url":"https://github.com/NJUVISION/MPA/blob/HEAD/seg_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.19613","paper":"/paper/hybrid-mamba-for-few-shot-segmentation","title":"Hybrid Mamba for Few-Shot Segmentation","date":"2024-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sam1224/hmnet","path":"model/resnet.py","file_url":"https://github.com/sam1224/hmnet/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.19613","paper":"/paper/hybrid-mamba-for-few-shot-segmentation","title":"Hybrid Mamba for Few-Shot Segmentation","date":"2024-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sam1224/hmnet","path":"model/backbone_res.py","file_url":"https://github.com/sam1224/hmnet/blob/HEAD/model/backbone_res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2409.17880","paper":"/paper/self-distilled-depth-refinement-with-noisy","title":"Self-Distilled Depth Refinement with Noisy Poisson Fusion","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijia7/sddr","path":"utils/Resnet.py","file_url":"https://github.com/lijia7/sddr/blob/HEAD/utils/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.12319","paper":"/paper/large-language-models-are-strong-audio-visual","title":"Large Language Models are Strong Audio-Visual Speech Recognition Learners","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umbertocappellazzo/llama-avsr","path":"av_hubert/avhubert/resnet.py","file_url":"https://github.com/umbertocappellazzo/llama-avsr/blob/HEAD/av_hubert/avhubert/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2409.12105","paper":"/paper/fedlf-adaptive-logit-adjustment-and-feature","title":"FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning","date":"2024-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"18sym/FedLF","path":"algorithm/fedlf.py","file_url":"https://github.com/18sym/FedLF/blob/HEAD/algorithm/fedlf.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2409.07446","paper":"/paper/adaptive-adapter-routing-for-long-tailed","title":"Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning","date":"2024-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-qzh/apart","path":"backbone/resnet.py","file_url":"https://github.com/vita-qzh/apart/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2409.06290","paper":"/paper/entaugment-entropy-driven-adaptive-data","title":"EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jackbrocp/entaugment","path":"Network/pyramidnet.py","file_url":"https://github.com/jackbrocp/entaugment/blob/HEAD/Network/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.06290","paper":"/paper/entaugment-entropy-driven-adaptive-data","title":"EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jackbrocp/entaugment","path":"Network/Wide_Resnet.py","file_url":"https://github.com/jackbrocp/entaugment/blob/HEAD/Network/Wide_Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2409.03368","paper":"/paper/training-free-conversion-of-pretrained-anns","title":"Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications","date":"2024-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"putshua/inference-scale-ann-snn","path":"ResNet.py","file_url":"https://github.com/putshua/inference-scale-ann-snn/blob/HEAD/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2409.01128","paper":"/paper/diffusion-driven-data-replay-a-novel-approach","title":"Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning","date":"2024-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinglin-liang/DDDR","path":"convs/resnet.py","file_url":"https://github.com/jinglin-liang/DDDR/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2409.01128","paper":"/paper/diffusion-driven-data-replay-a-novel-approach","title":"Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning","date":"2024-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinglin-liang/DDDR","path":"convs/resnet_cbam.py","file_url":"https://github.com/jinglin-liang/DDDR/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2409.01128","paper":"/paper/diffusion-driven-data-replay-a-novel-approach","title":"Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning","date":"2024-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinglin-liang/DDDR","path":"convs/modified_represnet.py","file_url":"https://github.com/jinglin-liang/DDDR/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2408.13983","paper":"/paper/dual-path-adversarial-lifting-for-domain","title":"Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation","date":"2024-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yushuntang/dpal","path":"models/Res.py","file_url":"https://github.com/yushuntang/dpal/blob/HEAD/models/Res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2408.13373","paper":"/paper/learning-unknowns-from-unknowns-diversified","title":"Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition","date":"2024-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icgy96/dnpg","path":"architectures/ResNetFeat.py","file_url":"https://github.com/icgy96/dnpg/blob/HEAD/architectures/ResNetFeat.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2408.10798","paper":"/paper/universal-novelty-detection-through-adaptive-1","title":"Universal Novelty Detection Through Adaptive Contrastive Learning","date":"2024-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mojtaba-nafez/unode","path":"models/custom_resnet.py","file_url":"https://github.com/mojtaba-nafez/unode/blob/HEAD/models/custom_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2408.08295","paper":"/paper/slca-unleash-the-power-of-sequential-fine","title":"SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengdavid/slca","path":"convs/resnet.py","file_url":"https://github.com/gengdavid/slca/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2408.06741","paper":"/paper/improving-synthetic-image-detection-towards","title":"Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ouxiang-li/safe","path":"models/resnet.py","file_url":"https://github.com/ouxiang-li/safe/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2408.05939","paper":"/paper/uniportrait-a-unified-framework-for-identity","title":"UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization","date":"2024-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junjiehe96/UniPortrait","path":"uniportrait/curricular_face/backbone/model_resnet.py","file_url":"https://github.com/junjiehe96/UniPortrait/blob/HEAD/uniportrait/curricular_face/backbone/model_resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6ccc5338f47a440c","mcp_get_code":{"code_sha256":"6ccc5338f47a440c"}},{"arxiv_id":"2408.05446","paper":"/paper/ensemble-everything-everywhere-multi-scale","title":"Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2408.04301","paper":"/paper/tackling-noisy-clients-in-federated-learning","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sprinter1999/fedelc","path":"model_resnet_official.py","file_url":"https://github.com/sprinter1999/fedelc/blob/HEAD/model_resnet_official.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2408.04301","paper":"/paper/tackling-noisy-clients-in-federated-learning","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sprinter1999/fedelc","path":"resnets/model_resnet.py","file_url":"https://github.com/sprinter1999/fedelc/blob/HEAD/resnets/model_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2408.04301","paper":"/paper/tackling-noisy-clients-in-federated-learning","title":"Tackling Noisy Clients in Federated Learning with End-to-end Label Correction","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sprinter1999/fedelc","path":"fl_models/nets.py","file_url":"https://github.com/sprinter1999/fedelc/blob/HEAD/fl_models/nets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70815f7b9640eb64","mcp_get_code":{"code_sha256":"70815f7b9640eb64"}},{"arxiv_id":"2408.01978","paper":"/paper/2408-01978","title":"AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning","date":"2024-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinwong/advqdet","path":"acpt/resnet.py","file_url":"https://github.com/xinwong/advqdet/blob/HEAD/acpt/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2408.01044","paper":"/paper/2408-01044","title":"Boosting Gaze Object Prediction via Pixel-level Supervision from Vision Foundation Model","date":"2024-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinyang06/SamGOP","path":"maskGOP/resnet.py","file_url":"https://github.com/jinyang06/SamGOP/blob/HEAD/maskGOP/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2408.00929","paper":"/paper/2408-00929","title":"Verification of Machine Unlearning is Fragile","date":"2024-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangbinchi/unlearning-verification-is-fragile","path":"model.py","file_url":"https://github.com/zhangbinchi/unlearning-verification-is-fragile/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.19514","paper":"/paper/detached-and-interactive-multimodal-learning","title":"Detached and Interactive Multimodal Learning","date":"2024-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanyunfeng-bit/di-mml","path":"models/backbone.py","file_url":"https://github.com/fanyunfeng-bit/di-mml/blob/HEAD/models/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2407.19507","paper":"/paper/wecromcl-weakly-supervised-cross-modality","title":"WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting","date":"2024-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"2407.19308","paper":"/paper/comprehensive-attribution-inherently","title":"Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector","date":"2024-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zood123/comet","path":"models/ResNetWithGate.py","file_url":"https://github.com/zood123/comet/blob/HEAD/models/ResNetWithGate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.18658","paper":"/paper/adversarial-robustification-via-text-to-image","title":"Adversarial Robustification via Text-to-Image Diffusion Models","date":"2024-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"choidae1/robustify-t2i","path":"classifiers/resnet.py","file_url":"https://github.com/choidae1/robustify-t2i/blob/HEAD/classifiers/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.16955","paper":"/paper/dvpe-divided-view-position-embedding-for","title":"DVPE: Divided View Position Embedding for Multi-View 3D Object Detection","date":"2024-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dop0/DVPE","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/dop0/DVPE/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2407.16802","paper":"/paper/distribution-aware-robust-learning-from-long","title":"Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JaesoonBaik1213/DaSC","path":"models/PreResNet.py","file_url":"https://github.com/JaesoonBaik1213/DaSC/blob/HEAD/models/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.15793","paper":"/paper/clip-with-generative-latent-replay-a-strong","title":"CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/mammoth","path":"backbone/ResNetBottleneck.py","file_url":"https://github.com/aimagelab/mammoth/blob/HEAD/backbone/ResNetBottleneck.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2407.15793","paper":"/paper/clip-with-generative-latent-replay-a-strong","title":"CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/mammoth","path":"backbone/ResNet32.py","file_url":"https://github.com/aimagelab/mammoth/blob/HEAD/backbone/ResNet32.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2407.15793","paper":"/paper/clip-with-generative-latent-replay-a-strong","title":"CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/mammoth","path":"backbone/ResNetBlock.py","file_url":"https://github.com/aimagelab/mammoth/blob/HEAD/backbone/ResNetBlock.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25e04c3b7a8cc075","mcp_get_code":{"code_sha256":"25e04c3b7a8cc075"}},{"arxiv_id":"2407.15085","paper":"/paper/learn-to-preserve-and-diversify-parameter","title":"Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization","date":"2024-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JudgingH/PEGO","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/JudgingH/PEGO/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2407.12538","paper":"/paper/high-frequency-matters-uncertainty-guided","title":"High Frequency Matters: Uncertainty Guided Image Compression with Wavelet Diffusion","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hejiaxiang1/wavelet-diffusion","path":"residualencoder/models/Resnet.py","file_url":"https://github.com/hejiaxiang1/wavelet-diffusion/blob/HEAD/residualencoder/models/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.12538","paper":"/paper/high-frequency-matters-uncertainty-guided","title":"High Frequency Matters: Uncertainty Guided Image Compression with Wavelet Diffusion","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hejiaxiang1/wavelet-diffusion","path":"residualencoder/layers/layers.py","file_url":"https://github.com/hejiaxiang1/wavelet-diffusion/blob/HEAD/residualencoder/layers/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cbeed6985b2cf30","mcp_get_code":{"code_sha256":"0cbeed6985b2cf30"}},{"arxiv_id":"2407.11910","paper":"/paper/benchmarking-the-attribution-quality-of","title":"Benchmarking the Attribution Quality of Vision Models","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/idsds","path":"models/resnet.py","file_url":"https://github.com/visinf/idsds/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2407.10918","paper":"/paper/partimagenet-dataset-scaling-up-part-based","title":"PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LixiaoTHU/PartImageNetPP","path":"model/resnet.py","file_url":"https://github.com/LixiaoTHU/PartImageNetPP/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2407.10876","paper":"/paper/repvf-a-unified-vector-fields-representation","title":"RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jbji/RepVF","path":"plugins/models/backbones/vovnet.py","file_url":"https://github.com/jbji/RepVF/blob/HEAD/plugins/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2407.09842","paper":"/paper/eliminating-feature-ambiguity-for-few-shot","title":"Eliminating Feature Ambiguity for Few-Shot Segmentation","date":"2024-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sam1224/aenet","path":"HDMNet/model/resnet.py","file_url":"https://github.com/sam1224/aenet/blob/HEAD/HDMNet/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.09705","paper":"/paper/diagnosing-and-re-learning-for-balanced","title":"Diagnosing and Re-learning for Balanced Multimodal Learning","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gewu-lab/diagnosing_relearning_eccv2024","path":"code/models/backbone.py","file_url":"https://github.com/gewu-lab/diagnosing_relearning_eccv2024/blob/HEAD/code/models/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2407.09059","paper":"/paper/domain-adaptive-video-deblurring-via-test","title":"Domain-adaptive Video Deblurring via Test-time Blurring","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jin-ting-he/dadeblur","path":"DeblurringModel/ESTRNN/arches.py","file_url":"https://github.com/jin-ting-he/dadeblur/blob/HEAD/DeblurringModel/ESTRNN/arches.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2407.08966","paper":"/paper/lapt-label-driven-automated-prompt-tuning-for","title":"LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ybzh/openood-vlm","path":"openood/networks/bit.py","file_url":"https://github.com/ybzh/openood-vlm/blob/HEAD/openood/networks/bit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2407.08127","paper":"/paper/prediction-exposes-your-face-black-box-model","title":"Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zllrunning/face-parsing.PyTorch","path":"resnet.py","file_url":"https://github.com/zllrunning/face-parsing.PyTorch/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.07582","paper":"/paper/tip-tabular-image-pre-training-for-multimodal","title":"TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siyi-wind/TIP","path":"models/Tip_utils/ResNet.py","file_url":"https://github.com/siyi-wind/TIP/blob/HEAD/models/Tip_utils/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.07307","paper":"/paper/dual-stage-hyperspectral-image-classification","title":"Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"laprf/DSTC","path":"models/backbones/resnet.py","file_url":"https://github.com/laprf/DSTC/blob/HEAD/models/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2407.07003","paper":"/paper/learning-to-complement-and-to-defer-to","title":"Learning to Complement and to Defer to Multiple Users","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengzhang37/lecodu","path":"PreResNet.py","file_url":"https://github.com/zhengzhang37/lecodu/blob/HEAD/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.06780","paper":"/paper/cola-conditional-dropout-and-language-driven","title":"CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ssecv/CoLA","path":"code/MindSpore/Net.py","file_url":"https://github.com/ssecv/CoLA/blob/HEAD/code/MindSpore/Net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.06504","paper":"/paper/reprogramming-distillation-for-medical","title":"Reprogramming Distillation for Medical Foundation Models","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/RD","path":"network/resnet.py","file_url":"https://github.com/MediaBrain-SJTU/RD/blob/HEAD/network/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.05316","paper":"/paper/leveraging-topological-guidance-for-improved","title":"Leveraging Topological Guidance for Improved Knowledge Distillation","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeunsom/TGD","path":"resnet.py","file_url":"https://github.com/jeunsom/TGD/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2407.05316","paper":"/paper/leveraging-topological-guidance-for-improved","title":"Leveraging Topological Guidance for Improved Knowledge Distillation","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeunsom/TGD","path":"resnet_cifar.py","file_url":"https://github.com/jeunsom/TGD/blob/HEAD/resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.03563","paper":"/paper/learning-video-temporal-dynamics-with-cross","title":"Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition","date":"2024-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungnyun/avsr-temporal-dynamics","path":"avhubert/resnet.py","file_url":"https://github.com/sungnyun/avsr-temporal-dynamics/blob/HEAD/avhubert/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2407.02013","paper":"/paper/digraf-diffeomorphic-graph-adaptive","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ipsitmantri/DiTASK","path":"models/seg_hrnet.py","file_url":"https://github.com/ipsitmantri/DiTASK/blob/HEAD/models/seg_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2407.01919","paper":"/paper/a-method-to-facilitate-membership-inference","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DependableSystemsLab/code_poison_MIA","path":"networks/wide_resnet.py","file_url":"https://github.com/DependableSystemsLab/code_poison_MIA/blob/HEAD/networks/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2407.00983","paper":"/paper/fairmedfm-fairness-benchmarking-for-medical","title":"FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FairMedFM/FairMedFM","path":"models/c2l.py","file_url":"https://github.com/FairMedFM/FairMedFM/blob/HEAD/models/c2l.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.19931","paper":"/paper/decoupling-general-and-personalized-knowledge","title":"Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition","date":"2024-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinghaowu/feddecomp","path":"models/resnet.py","file_url":"https://github.com/xinghaowu/feddecomp/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2406.19435","paper":"/paper/a-sanity-check-for-ai-generated-image","title":"A Sanity Check for AI-generated Image Detection","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shilinyan99/aide","path":"models/AIDE.py","file_url":"https://github.com/shilinyan99/aide/blob/HEAD/models/AIDE.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.16231","paper":"/paper/gradual-divergence-for-seamless-adaptation-a","title":"Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/dare","path":"backbone/ResNet18.py","file_url":"https://github.com/neurai-lab/dare/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2406.15664","paper":"/paper/flat-posterior-does-matter-for-bayesian","title":"Flat Posterior Does Matter For Bayesian Model Averaging","date":"2024-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlai-yonsei/sa-bma","path":"utils/models/resnet.py","file_url":"https://github.com/mlai-yonsei/sa-bma/blob/HEAD/utils/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2406.13392","paper":"/paper/strengthening-layer-interaction-via-dynamic","title":"Strengthening Layer Interaction via Dynamic Layer Attention","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tunantu/dynamic-layer-attention","path":"image_classification/imagenet/models/resnet.py","file_url":"https://github.com/tunantu/dynamic-layer-attention/blob/HEAD/image_classification/imagenet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2406.13392","paper":"/paper/strengthening-layer-interaction-via-dynamic","title":"Strengthening Layer Interaction via Dynamic Layer Attention","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tunantu/dynamic-layer-attention","path":"image_classification/cifar/models/cifar/dia_resnet.py","file_url":"https://github.com/tunantu/dynamic-layer-attention/blob/HEAD/image_classification/cifar/models/cifar/dia_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.13392","paper":"/paper/strengthening-layer-interaction-via-dynamic","title":"Strengthening Layer Interaction via Dynamic Layer Attention","date":"2024-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tunantu/dynamic-layer-attention","path":"image_classification/cifar/models/cifar/dla_b.py","file_url":"https://github.com/tunantu/dynamic-layer-attention/blob/HEAD/image_classification/cifar/models/cifar/dla_b.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2406.12837","paper":"/paper/layermerge-neural-network-depth-compression","title":"LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/LayerMerge","path":"HALP/models/resnet.py","file_url":"https://github.com/snu-mllab/LayerMerge/blob/HEAD/HALP/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2406.12150","paper":"/paper/chaosmining-a-benchmark-to-evaluate-post-hoc","title":"ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"geshijoker/chaosmining","path":"chaosmining/vision/models.py","file_url":"https://github.com/geshijoker/chaosmining/blob/HEAD/chaosmining/vision/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2406.11711","paper":"/paper/ogni-dc-robust-depth-completion-with","title":"OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/OGNI-DC","path":"src/model/resnet_cbam.py","file_url":"https://github.com/princeton-vl/OGNI-DC/blob/HEAD/src/model/resnet_cbam.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"765f415ffbb2b6c9","mcp_get_code":{"code_sha256":"765f415ffbb2b6c9"}},{"arxiv_id":"2406.10427","paper":"/paper/adaptive-randomized-smoothing-certifying","title":"Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ubc-systopia/adaptive-randomized-smoothing","path":"src/models/cifar_resnet.py","file_url":"https://github.com/ubc-systopia/adaptive-randomized-smoothing/blob/HEAD/src/models/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.10391","paper":"/paper/beacon-benchmark-for-comprehensive-rna-tasks","title":"BEACON: Benchmark for Comprehensive RNA Tasks and Language Models","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"terry-r123/RNABenchmark","path":"downstream/structure/resnet.py","file_url":"https://github.com/terry-r123/RNABenchmark/blob/HEAD/downstream/structure/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2406.10082","paper":"/paper/whisper-flamingo-integrating-visual-features","title":"Whisper-Flamingo: Integrating Visual Features into Whisper for Audio-Visual Speech Recognition and Translation","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"roudimit/whisper-flamingo","path":"whisper/resnet.py","file_url":"https://github.com/roudimit/whisper-flamingo/blob/HEAD/whisper/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2406.04312","paper":"/paper/reno-enhancing-one-step-text-to-image-models","title":"ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/DOODL","path":"memcnn/models/resnet.py","file_url":"https://github.com/salesforce/DOODL/blob/HEAD/memcnn/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.02309","paper":"/paper/effects-of-exponential-gaussian-distribution","title":"Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tdano1/eg-on-smoothing","path":"archs/cifar_resnet.py","file_url":"https://github.com/tdano1/eg-on-smoothing/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2406.02309","paper":"/paper/effects-of-exponential-gaussian-distribution","title":"Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tdano1/eg-on-smoothing","path":"archs/wide_resnet_imagenet64.py","file_url":"https://github.com/tdano1/eg-on-smoothing/blob/HEAD/archs/wide_resnet_imagenet64.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2406.01063","paper":"/paper/dance-dual-view-distribution-alignment-for","title":"DANCE: Dual-View Distribution Alignment for Dataset Condensation","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hansong-Zhang/DANCE","path":"models/resnet.py","file_url":"https://github.com/Hansong-Zhang/DANCE/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.20610","paper":"/paper/revisiting-and-maximizing-temporal-knowledge","title":"Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wooseok-shin/PrevMatch","path":"model/backbone/resnet.py","file_url":"https://github.com/wooseok-shin/PrevMatch/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2405.19707","paper":"/paper/demamba-ai-generated-video-detection-on","title":"DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhaoxing/DeMamba","path":"models/NPR.py","file_url":"https://github.com/chenhaoxing/DeMamba/blob/HEAD/models/NPR.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.19291","paper":"/paper/grasp-as-you-say-language-guided-dexterous","title":"Grasp as You Say: Language-guided Dexterous Grasp Generation","date":null,"month_inferred_from_arxiv_id":"2024-05","title_source":"archive","repo":"iSEE-Laboratory/Grasp-as-You-Say","path":"model/backbone/resnet.py","file_url":"https://github.com/iSEE-Laboratory/Grasp-as-You-Say/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.19074","paper":"/paper/resurrecting-old-classes-with-new-data-for","title":"Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dipamgoswami/adc","path":"convs/modified_represnet.py","file_url":"https://github.com/dipamgoswami/adc/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2405.19055","paper":"/paper/fusu-a-multi-temporal-source-land-use-change","title":"FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanshuai0914/fusu","path":"models/backbone/hrnet.py","file_url":"https://github.com/yuanshuai0914/fusu/blob/HEAD/models/backbone/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.19055","paper":"/paper/fusu-a-multi-temporal-source-land-use-change","title":"FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanshuai0914/fusu","path":"models/backbone/resnet.py","file_url":"https://github.com/yuanshuai0914/fusu/blob/HEAD/models/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2405.19055","paper":"/paper/fusu-a-multi-temporal-source-land-use-change","title":"FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanshuai0914/fusu","path":"models/block/conv.py","file_url":"https://github.com/yuanshuai0914/fusu/blob/HEAD/models/block/conv.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17071f01b62cbc8d","mcp_get_code":{"code_sha256":"17071f01b62cbc8d"}},{"arxiv_id":"2405.18861","paper":"/paper/domain-inspired-sharpness-aware-minimization","title":"Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/DISAM","path":"code/models/resnet.py","file_url":"https://github.com/MediaBrain-SJTU/DISAM/blob/HEAD/code/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe58fe326c4a2573","mcp_get_code":{"code_sha256":"fe58fe326c4a2573"}},{"arxiv_id":"2405.17022","paper":"/paper/compositional-few-shot-class-incremental","title":"Compositional Few-Shot Class-Incremental Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/comp-fscil","path":"models/resnet18_encoder.py","file_url":"https://github.com/zoilsen/comp-fscil/blob/HEAD/models/resnet18_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.17022","paper":"/paper/compositional-few-shot-class-incremental","title":"Compositional Few-Shot Class-Incremental Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/comp-fscil","path":"models/resnet20_cifar.py","file_url":"https://github.com/zoilsen/comp-fscil/blob/HEAD/models/resnet20_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.17022","paper":"/paper/compositional-few-shot-class-incremental","title":"Compositional Few-Shot Class-Incremental Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/comp-fscil","path":"models/resnet12.py","file_url":"https://github.com/zoilsen/comp-fscil/blob/HEAD/models/resnet12.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"2405.16002","paper":"/paper/does-sgd-really-happen-in-tiny-subspaces","title":"Does SGD really happen in tiny subspaces?","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/edge-of-stability","path":"src/resnet_cifar.py","file_url":"https://github.com/locuslab/edge-of-stability/blob/HEAD/src/resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.15020","paper":"/paper/adjointdeis-efficient-gradients-for-diffusion","title":"AdjointDEIS: Efficient Gradients for Diffusion Models","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fdbtrs/ElasticFace","path":"backbones/iresnet.py","file_url":"https://github.com/fdbtrs/ElasticFace/blob/HEAD/backbones/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2405.15020","paper":"/paper/adjointdeis-efficient-gradients-for-diffusion","title":"AdjointDEIS: Efficient Gradients for Diffusion Models","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fdbtrs/ElasticFace","path":"backbones/utils.py","file_url":"https://github.com/fdbtrs/ElasticFace/blob/HEAD/backbones/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"be7e40142c49c8be","mcp_get_code":{"code_sha256":"be7e40142c49c8be"}},{"arxiv_id":"2405.14793","paper":"/paper/sea-raft-simple-efficient-accurate-raft-for","title":"SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/sea-raft","path":"core/layer.py","file_url":"https://github.com/princeton-vl/sea-raft/blob/HEAD/core/layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"7599edadbb4f2bdc","mcp_get_code":{"code_sha256":"7599edadbb4f2bdc"}},{"arxiv_id":"2405.14325","paper":"/paper/dinomaly-the-less-is-more-philosophy-in-multi","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guojiajeremy/dinomaly","path":"models/de_resnet.py","file_url":"https://github.com/guojiajeremy/dinomaly/blob/HEAD/models/de_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2405.09321","paper":"/paper/reconboost-boosting-can-achieve-modality","title":"ReconBoost: Boosting Can Achieve Modality Reconcilement","date":"2024-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huacong/reconboost","path":"models/CREMA/backbone.py","file_url":"https://github.com/huacong/reconboost/blob/HEAD/models/CREMA/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.06880","paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sldgroup/emcad","path":"lib/resnet.py","file_url":"https://github.com/sldgroup/emcad/blob/HEAD/lib/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.06283","paper":"/paper/novel-class-discovery-for-ultra-fine-grained","title":"Novel Class Discovery for Ultra-Fine-Grained Visual Categorization","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SSDUT-Caiyq/UFG-NCD","path":"models/resnet50.py","file_url":"https://github.com/SSDUT-Caiyq/UFG-NCD/blob/HEAD/models/resnet50.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2405.05808","paper":"/paper/fast-and-controllable-post-training-sparsity","title":"Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ModelTC/FCPTS","path":"models/resnet.py","file_url":"https://github.com/ModelTC/FCPTS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2405.05259","paper":"/paper/openess-event-based-semantic-scene","title":"OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies","date":"2024-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ldkong1205/openess","path":"models/_resnet.py","file_url":"https://github.com/ldkong1205/openess/blob/HEAD/models/_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.03649","paper":"/paper/learning-robust-classifiers-with-self-guided","title":"Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtzheng/LBC","path":"models/resnet.py","file_url":"https://github.com/gtzheng/LBC/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.03590","paper":"/paper/deep-clustering-with-self-supervision-using","title":"Deep Clustering with Self-Supervision using Pairwise Similarities","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Armanfard-Lab/DCSS","path":"Codes/Network.py","file_url":"https://github.com/Armanfard-Lab/DCSS/blob/HEAD/Codes/Network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.03299","paper":"/paper/darkfed-a-data-free-backdoor-attack-in","title":"DarkFed: A Data-Free Backdoor Attack in Federated Learning","date":null,"month_inferred_from_arxiv_id":"2024-05","title_source":"archive","repo":"hustweiwan/DarkFed","path":"models/resnet.py","file_url":"https://github.com/hustweiwan/DarkFed/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2405.03299","paper":"/paper/darkfed-a-data-free-backdoor-attack-in","title":"DarkFed: A Data-Free Backdoor Attack in Federated Learning","date":null,"month_inferred_from_arxiv_id":"2024-05","title_source":"archive","repo":"hustweiwan/DarkFed","path":"models/resnet_s.py","file_url":"https://github.com/hustweiwan/DarkFed/blob/HEAD/models/resnet_s.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2405.02581","paper":"/paper/stationary-representations-optimally","title":"Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements","date":"2024-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"miccunifi/iamcl2r","path":"src/iamcl2r/models/resnet.py","file_url":"https://github.com/miccunifi/iamcl2r/blob/HEAD/src/iamcl2r/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2405.01460","paper":"/paper/purify-unlearnable-examples-via-rate","title":"Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuyi-sd/D-VAE","path":"models/resnet_official.py","file_url":"https://github.com/yuyi-sd/D-VAE/blob/HEAD/models/resnet_official.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2404.19651","paper":"/paper/provably-robust-conformal-prediction-with","title":"Provably Robust Conformal Prediction with Improved Efficiency","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustworthy-ml-lab/provably-robust-conformal-prediction","path":"code/Architectures/ResNet.py","file_url":"https://github.com/trustworthy-ml-lab/provably-robust-conformal-prediction/blob/HEAD/code/Architectures/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.17617","paper":"/paper/beyond-traditional-threats-a-persistent","title":"Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning","date":"2024-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PhD-TaoLiu/FCBA","path":"models/pytorch_resnet.py","file_url":"https://github.com/PhD-TaoLiu/FCBA/blob/HEAD/models/pytorch_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.16277","paper":"/paper/causally-inspired-regularization-enables","title":"Causally Inspired Regularization Enables Domain General Representations","date":"2024-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"olawalesalaudeen/tcri","path":"DomainBed/domainbed/lib/wide_resnet.py","file_url":"https://github.com/olawalesalaudeen/tcri/blob/HEAD/DomainBed/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2404.14829","paper":"/paper/revisiting-neural-networks-for-continual","title":"Revisiting Neural Networks for Continual Learning: An Architectural Perspective","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"byyx666/archcraft","path":"class_il/convs/memo_resnet.py","file_url":"https://github.com/byyx666/archcraft/blob/HEAD/class_il/convs/memo_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.14829","paper":"/paper/revisiting-neural-networks-for-continual","title":"Revisiting Neural Networks for Continual Learning: An Architectural Perspective","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"byyx666/archcraft","path":"class_il/convs/modified_represnet.py","file_url":"https://github.com/byyx666/archcraft/blob/HEAD/class_il/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2404.13904","paper":"/paper/deep-regression-representation-learning-with","title":"Deep Regression Representation Learning with Topology","date":"2024-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"needylove/PH-Reg","path":"agedb-dir/resnet.py","file_url":"https://github.com/needylove/PH-Reg/blob/HEAD/agedb-dir/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.13640","paper":"/paper/beyond-alignment-blind-video-face-restoration","title":"Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer","date":"2024-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kepengxu/PGTFormer","path":"archs/pgtformer_arch.py","file_url":"https://github.com/kepengxu/PGTFormer/blob/HEAD/archs/pgtformer_arch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.11003","paper":"/paper/infomatch-entropy-neural-estimation-for-semi","title":"InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunzhan/infomatch","path":"models/resnext.py","file_url":"https://github.com/kunzhan/infomatch/blob/HEAD/models/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2404.09692","paper":"/paper/xoftr-cross-modal-feature-matching","title":"XoFTR: Cross-modal Feature Matching Transformer","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ondert/xoftr","path":"src/xoftr/backbone/resnet.py","file_url":"https://github.com/ondert/xoftr/blob/HEAD/src/xoftr/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.09586","paper":"/paper/mitigating-the-curse-of-dimensionality-for","title":"Mitigating the Curse of Dimensionality for Certified Robustness via Dual Randomized Smoothing","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiasong0501/DRS","path":"archs/cifar_resnet.py","file_url":"https://github.com/xiasong0501/DRS/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.06842","paper":"/paper/mocha-stereo-motif-channel-attention-network","title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyangchen/mocha-stereo","path":"MoCha-Stereo/nets/feature.py","file_url":"https://github.com/zyangchen/mocha-stereo/blob/HEAD/MoCha-Stereo/nets/feature.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fba66e062846970","mcp_get_code":{"code_sha256":"9fba66e062846970"}},{"arxiv_id":"2404.06622","paper":"/paper/calibrating-higher-order-statistics-for-few","title":"Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dipamgoswami/fscil-calibration","path":"backbone/resnet.py","file_url":"https://github.com/dipamgoswami/fscil-calibration/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.06564","paper":"/paper/mambaad-exploring-state-space-models-for","title":"MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lewandofskee/MambaAD","path":"model/mambaad.py","file_url":"https://github.com/lewandofskee/MambaAD/blob/HEAD/model/mambaad.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2404.06287","paper":"/paper/counterfactual-reasoning-for-multi-label","title":"Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiemk/mlc-pat","path":"src_files/models/resnet.py","file_url":"https://github.com/xiemk/mlc-pat/blob/HEAD/src_files/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.06025","paper":"/paper/greedy-dim-greedy-algorithms-for-unreasonably","title":"Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs","date":"2024-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zblasingame/Greedy-DiM","path":"arcface/iresnet.py","file_url":"https://github.com/zblasingame/Greedy-DiM/blob/HEAD/arcface/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2404.05440","paper":"/paper/tree-search-based-policy-optimization-under","title":"Tree Search-Based Policy Optimization under Stochastic Execution Delay","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidva1/Delayed-EZ","path":"config/atari/model.py","file_url":"https://github.com/davidva1/Delayed-EZ/blob/HEAD/config/atari/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2404.04819","paper":"/paper/joint-reconstruction-of-3d-human-and-object","title":"Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer","date":"2024-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dqj5182/contho_release","path":"lib/models/resnet.py","file_url":"https://github.com/dqj5182/contho_release/blob/HEAD/lib/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.04002","paper":"/paper/continual-learning-with-weight-interpolation","title":"Continual Learning with Weight Interpolation","date":"2024-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jedrzejkozal/weight-interpolation-cl","path":"backbone/ResNet18.py","file_url":"https://github.com/jedrzejkozal/weight-interpolation-cl/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2404.02719","paper":"/paper/can-we-understand-plasticity-through-neural","title":"Can We Understand Plasticity Through Neural Collapse?","date":"2024-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gianhess/dl_project","path":"experiments/warm_up/utils/resnet.py","file_url":"https://github.com/gianhess/dl_project/blob/HEAD/experiments/warm_up/utils/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.02117","paper":"/paper/pre-trained-vision-and-language-transformers","title":"Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KHU-AGI/PriViLege","path":"models/resnet18_encoder.py","file_url":"https://github.com/KHU-AGI/PriViLege/blob/HEAD/models/resnet18_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.02117","paper":"/paper/pre-trained-vision-and-language-transformers","title":"Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KHU-AGI/PriViLege","path":"models/resnet20_cifar.py","file_url":"https://github.com/KHU-AGI/PriViLege/blob/HEAD/models/resnet20_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.01692","paper":"/paper/beyond-image-super-resolution-for-image","title":"Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JaehaKim97/SR4IR","path":"src/archs/cls/resnet.py","file_url":"https://github.com/JaehaKim97/SR4IR/blob/HEAD/src/archs/cls/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2404.00986","paper":"/paper/make-continual-learning-stronger-via-c-flat","title":"Make Continual Learning Stronger via C-Flat","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wannaa/c-flat","path":"convs/memo_resnet.py","file_url":"https://github.com/wannaa/c-flat/blob/HEAD/convs/memo_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2404.00986","paper":"/paper/make-continual-learning-stronger-via-c-flat","title":"Make Continual Learning Stronger via C-Flat","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wannaa/c-flat","path":"convs/resnet_cbam.py","file_url":"https://github.com/wannaa/c-flat/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.00986","paper":"/paper/make-continual-learning-stronger-via-c-flat","title":"Make Continual Learning Stronger via C-Flat","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wannaa/c-flat","path":"convs/modified_represnet.py","file_url":"https://github.com/wannaa/c-flat/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2404.00876","paper":"/paper/mgmap-mask-guided-learning-for-online","title":"MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaolul2/MGMap","path":"projects/mmdet3d_plugin/mgmap/modules/bevencode_multi.py","file_url":"https://github.com/xiaolul2/MGMap/blob/HEAD/projects/mmdet3d_plugin/mgmap/modules/bevencode_multi.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2404.00380","paper":"/paper/dhr-dual-features-driven-hierarchical","title":"DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation","date":"2024-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shjo-april/DHR","path":"core/backbones/resnet/model.py","file_url":"https://github.com/shjo-april/DHR/blob/HEAD/core/backbones/resnet/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2404.00230","paper":"/paper/latent-watermark-inject-and-detect-watermarks","title":"Latent Watermark: Inject and Detect Watermarks in Latent Diffusion Space","date":"2024-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"richardsunnymeng/latentwatermark","path":"models/extractors/naive.py","file_url":"https://github.com/richardsunnymeng/latentwatermark/blob/HEAD/models/extractors/naive.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d33c6bd60a77ad21","mcp_get_code":{"code_sha256":"d33c6bd60a77ad21"}},{"arxiv_id":"2403.20320","paper":"/paper/mtlora-a-low-rank-adaptation-approach-for","title":"MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scale-lab/mtlora","path":"models/seg_hrnet.py","file_url":"https://github.com/scale-lab/mtlora/blob/HEAD/models/seg_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.19926","paper":"/paper/video-based-human-pose-regression-via","title":"Video-Based Human Pose Regression via Decoupled Space-Time Aggregation","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zgspose/DSTA","path":"posetimation/layers/basic_model.py","file_url":"https://github.com/zgspose/DSTA/blob/HEAD/posetimation/layers/basic_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e9bddedbc350840c","mcp_get_code":{"code_sha256":"e9bddedbc350840c"}},{"arxiv_id":"2403.19067","paper":"/paper/low-rank-rescaled-vision-transformer-fine","title":"Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zstarn70/rlrr","path":"models/RLRR_ResNet.py","file_url":"https://github.com/zstarn70/rlrr/blob/HEAD/models/RLRR_ResNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c2f1c60cba5ded82","mcp_get_code":{"code_sha256":"c2f1c60cba5ded82"}},{"arxiv_id":"2403.18886","paper":"/paper/self-expansion-of-pre-trained-models-with","title":"Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huiyiwang01/sema-cl","path":"backbone/resnet.py","file_url":"https://github.com/huiyiwang01/sema-cl/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.18274","paper":"/paper/dvlo-deep-visual-lidar-odometry-with-local-to","title":"DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignment","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IRMVLab/DVLO","path":"conv_util.py","file_url":"https://github.com/IRMVLab/DVLO/blob/HEAD/conv_util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.17465","paper":"/paper/lare-2-latent-reconstruction-error-based","title":"LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luo3300612/lare","path":"model_base.py","file_url":"https://github.com/luo3300612/lare/blob/HEAD/model_base.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6978f1d410660f53","mcp_get_code":{"code_sha256":"6978f1d410660f53"}},{"arxiv_id":"2403.16510","paper":"/paper/make-your-anchor-a-diffusion-based-2d-avatar","title":"Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework","date":"2024-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ictmcg/make-your-anchor","path":"inference/resnet.py","file_url":"https://github.com/ictmcg/make-your-anchor/blob/HEAD/inference/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.15751","paper":"/paper/aocil-exemplar-free-analytic-online-class","title":"F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning","date":"2024-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyuchen-cz/f-oal","path":"models/resnet.py","file_url":"https://github.com/liuyuchen-cz/f-oal/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2403.14729","paper":"/paper/auto-train-once-controller-network-guided","title":"Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xidongwu/autotrainonce","path":"imgnet_models/resnet_gate.py","file_url":"https://github.com/xidongwu/autotrainonce/blob/HEAD/imgnet_models/resnet_gate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.14349","paper":"/paper/on-the-concept-trustworthiness-in-concept","title":"On the Concept Trustworthiness in Concept Bottleneck Models","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hqhQAQ/ProtoCBM","path":"models/resnet_features_all.py","file_url":"https://github.com/hqhQAQ/ProtoCBM/blob/HEAD/models/resnet_features_all.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.14101","paper":"/paper/text-enhanced-data-free-approach-for","title":"Text-Enhanced Data-free Approach for Federated Class-Incremental Learning","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmtuan1307/lander","path":"convs/resnet.py","file_url":"https://github.com/tmtuan1307/lander/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.14101","paper":"/paper/text-enhanced-data-free-approach-for","title":"Text-Enhanced Data-free Approach for Federated Class-Incremental Learning","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmtuan1307/lander","path":"convs/resnet_cbam.py","file_url":"https://github.com/tmtuan1307/lander/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.14101","paper":"/paper/text-enhanced-data-free-approach-for","title":"Text-Enhanced Data-free Approach for Federated Class-Incremental Learning","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmtuan1307/lander","path":"convs/modified_represnet.py","file_url":"https://github.com/tmtuan1307/lander/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2403.13249","paper":"/paper/a-unified-and-general-framework-for-continual","title":"A Unified and General Framework for Continual Learning","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/CL-refresh-learning","path":"backbone/ResNet18.py","file_url":"https://github.com/joey-wang123/CL-refresh-learning/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2403.12350","paper":"/paper/friendly-sharpness-aware-minimization","title":"Friendly Sharpness-Aware Minimization","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nblt/F-SAM","path":"models/pyramidnet.py","file_url":"https://github.com/nblt/F-SAM/blob/HEAD/models/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.11708","paper":"/paper/implicit-discriminative-knowledge-learning","title":"Implicit Discriminative Knowledge Learning for Visible-Infrared Person Re-Identification","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1kk077/idkl","path":"IDKL/models/resnet.py","file_url":"https://github.com/1kk077/idkl/blob/HEAD/IDKL/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.11650","paper":"/paper/prioritized-semantic-learning-for-zero-shot","title":"Prioritized Semantic Learning for Zero-shot Instance Navigation","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XinyuSun/PSL-InstanceNav","path":"PSL/models/resnet_gn.py","file_url":"https://github.com/XinyuSun/PSL-InstanceNav/blob/HEAD/PSL/models/resnet_gn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.11650","paper":"/paper/prioritized-semantic-learning-for-zero-shot","title":"Prioritized Semantic Learning for Zero-shot Instance Navigation","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XinyuSun/PSL-InstanceNav","path":"PSL/models/resnet_zer.py","file_url":"https://github.com/XinyuSun/PSL-InstanceNav/blob/HEAD/PSL/models/resnet_zer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e9bddedbc350840c","mcp_get_code":{"code_sha256":"e9bddedbc350840c"}},{"arxiv_id":"2403.11561","paper":"/paper/learning-unified-reference-representation-for","title":"Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlr7999/rlr","path":"models/decoder.py","file_url":"https://github.com/hlr7999/rlr/blob/HEAD/models/decoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2db7a58e9288f8c7","mcp_get_code":{"code_sha256":"2db7a58e9288f8c7"}},{"arxiv_id":"2403.11348","paper":"/paper/colep-certifiably-robust-learning-reasoning","title":"COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits","date":"2024-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangmintong/COLEP","path":"archs/cifar_resnet.py","file_url":"https://github.com/kangmintong/COLEP/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.10391","paper":"/paper/cdmad-class-distribution-mismatch-aware","title":"CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeeHyuck/CDMAD","path":"wrn.py","file_url":"https://github.com/LeeHyuck/CDMAD/blob/HEAD/wrn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4917ae1314a3442","mcp_get_code":{"code_sha256":"a4917ae1314a3442"}},{"arxiv_id":"2403.10045","paper":"/paper/towards-adversarially-robust-dataset","title":"Towards Adversarially Robust Dataset Distillation by Curvature Regularization","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yumozi/guard","path":"models/resnet.py","file_url":"https://github.com/yumozi/guard/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.09274","paper":"/paper/eventrpg-event-data-augmentation-with","title":"EventRPG: Event Data Augmentation with Relevance Propagation Guidance","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"myuansun/EventRPG","path":"snn_utils/sew_resnet.py","file_url":"https://github.com/myuansun/EventRPG/blob/HEAD/snn_utils/sew_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9e146fe9b081dfa","mcp_get_code":{"code_sha256":"e9e146fe9b081dfa"}},{"arxiv_id":"2403.09274","paper":"/paper/eventrpg-event-data-augmentation-with","title":"EventRPG: Event Data Augmentation with Relevance Propagation Guidance","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"myuansun/EventRPG","path":"utils/resnet.py","file_url":"https://github.com/myuansun/EventRPG/blob/HEAD/utils/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c930c4c4b347fdeb","mcp_get_code":{"code_sha256":"c930c4c4b347fdeb"}},{"arxiv_id":"2403.08649","paper":"/paper/a-causal-inspired-early-branching-structure","title":"A Causal Inspired Early-Branching Structure for Domain Generalization","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangchen527/causeb","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/liangchen527/causeb/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2403.07968","paper":"/paper/do-deep-neural-network-solutions-form-a-star","title":"Do Deep Neural Network Solutions Form a Star Domain?","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aktsonthalia/starlight","path":"models/wide_resnet.py","file_url":"https://github.com/aktsonthalia/starlight/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2403.07359","paper":"/paper/fsc-few-point-shape-completion","title":"FSC: Few-point Shape Completion","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xianzuwu/FSC","path":"models/resnet.py","file_url":"https://github.com/xianzuwu/FSC/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.06726","paper":"/paper/probabilistic-contrastive-learning-for-long","title":"Probabilistic Contrastive Learning for Long-Tailed Visual Recognition","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/proco","path":"ProCo/models/resnext.py","file_url":"https://github.com/leaplabthu/proco/blob/HEAD/ProCo/models/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2403.06098","paper":"/paper/vidprom-a-million-scale-real-prompt-gallery","title":"VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ekko-zn/AIGCDetectBenchmark","path":"networks/resnet_gram.py","file_url":"https://github.com/Ekko-zn/AIGCDetectBenchmark/blob/HEAD/networks/resnet_gram.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.06098","paper":"/paper/vidprom-a-million-scale-real-prompt-gallery","title":"VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ekko-zn/AIGCDetectBenchmark","path":"networks/resnet.py","file_url":"https://github.com/Ekko-zn/AIGCDetectBenchmark/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.06093","paper":"/paper/enhancing-3d-object-detection-with-2d","title":"Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nullmax-vision/QAF2D","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/nullmax-vision/QAF2D/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2403.06075","paper":"/paper/multisize-dataset-condensation","title":"Multisize Dataset Condensation","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/Multisize-Dataset-Condensation","path":"models/resnet.py","file_url":"https://github.com/he-y/Multisize-Dataset-Condensation/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.06014","paper":"/paper/hard-label-based-small-query-black-box","title":"Hard-label based Small Query Black-box Adversarial Attack","date":"2024-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jpark04-qub/sqba","path":"net/cifar10/resnet.py","file_url":"https://github.com/jpark04-qub/sqba/blob/HEAD/net/cifar10/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.02886","paper":"/paper/revisiting-confidence-estimation-towards","title":"Revisiting Confidence Estimation: Towards Reliable Failure Prediction","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"impression2805/fmfp","path":"model/resnet.py","file_url":"https://github.com/impression2805/fmfp/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2403.02690","paper":"/paper/dirichlet-based-per-sample-weighting-by","title":"Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaeHeeSun/RENT","path":"RENT/network/ResNet.py","file_url":"https://github.com/BaeHeeSun/RENT/blob/HEAD/RENT/network/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.02689","paper":"/paper/deep-common-feature-mining-for-efficient","title":"Deep Common Feature Mining for Efficient Video Semantic Segmentation","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buaahugegun/dcfm","path":"nets/hrnet/seg_hrnet.py","file_url":"https://github.com/buaahugegun/dcfm/blob/HEAD/nets/hrnet/seg_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.02628","paper":"/paper/interactive-continual-learning-fast-and-slow","title":"Interactive Continual Learning: Fast and Slow Thinking","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biqing-qi/interactive-continual-learning-fast-and-slow-thinking","path":"backbone/ResNet18.py","file_url":"https://github.com/biqing-qi/interactive-continual-learning-fast-and-slow-thinking/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2403.01510","paper":"/paper/end-to-end-human-instance-matting","title":"End-to-End Human Instance Matting","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qlyoo/e2e-him","path":"matmodel.py","file_url":"https://github.com/qlyoo/e2e-him/blob/HEAD/matmodel.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2403.01238","paper":"/paper/on-the-road-to-portability-compressing-end-to","title":"On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tulerfeng/PlanKD","path":"interfuser/plankd.py","file_url":"https://github.com/tulerfeng/PlanKD/blob/HEAD/interfuser/plankd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7e98e1d315e2ea27","mcp_get_code":{"code_sha256":"7e98e1d315e2ea27"}},{"arxiv_id":"2403.01189","paper":"/paper/training-unbiased-diffusion-models-from","title":"Training Unbiased Diffusion Models From Biased Dataset","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ermongroup/fairgen","path":"src/clf_models.py","file_url":"https://github.com/ermongroup/fairgen/blob/HEAD/src/clf_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2403.01101","paper":"/paper/feature-alignment-rethinking-efficient-active","title":"Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZiTingW/asvp","path":"cifar_resnet_1.py","file_url":"https://github.com/ZiTingW/asvp/blob/HEAD/cifar_resnet_1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2403.00628","paper":"/paper/region-adaptive-transform-with-segmentation","title":"Region-Adaptive Transform with Segmentation Prior for Image Compression","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GityuxiLiu/Region-Adaptive-Transform-with-Segmentation-Prior-for-Image-Compression","path":"SegPIC-main/compressai/layers/layers.py","file_url":"https://github.com/GityuxiLiu/Region-Adaptive-Transform-with-Segmentation-Prior-for-Image-Compression/blob/HEAD/SegPIC-main/compressai/layers/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cbeed6985b2cf30","mcp_get_code":{"code_sha256":"0cbeed6985b2cf30"}},{"arxiv_id":"2403.00564","paper":"/paper/efficientzero-v2-mastering-discrete-and","title":"EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shengjiewang-jason/efficientzerov2","path":"ez/agents/models/layer.py","file_url":"https://github.com/shengjiewang-jason/efficientzerov2/blob/HEAD/ez/agents/models/layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2403.00329","paper":"/paper/learning-with-logical-constraints-but-without","title":"Learning with Logical Constraints but without Shortcut Satisfaction","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SoftWiser-group/NeSy-without-Shortcuts","path":"models/resnet.py","file_url":"https://github.com/SoftWiser-group/NeSy-without-Shortcuts/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2403.07912","paper":"/paper/handgcat-occlusion-robust-3d-hand-mesh","title":"HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"heartstrive/handgcat","path":"common/nets/backbone.py","file_url":"https://github.com/heartstrive/handgcat/blob/HEAD/common/nets/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2402.19197","paper":"/paper/fine-structure-aware-sampling-a-new-sampling","title":"Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kcyt/FSS","path":"lib/model/HGPIFuNetwNML.py","file_url":"https://github.com/kcyt/FSS/blob/HEAD/lib/model/HGPIFuNetwNML.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3284016a26f2c98c","mcp_get_code":{"code_sha256":"3284016a26f2c98c"}},{"arxiv_id":"2402.19091","paper":"/paper/leveraging-representations-from-intermediate","title":"Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mever-team/rine","path":"src/extras.py","file_url":"https://github.com/mever-team/rine/blob/HEAD/src/extras.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.17726","paper":"/paper/vrp-sam-sam-with-visual-reference-prompt","title":"VRP-SAM: SAM with Visual Reference Prompt","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syp2ysy/vrp-sam","path":"model/base/resnet.py","file_url":"https://github.com/syp2ysy/vrp-sam/blob/HEAD/model/base/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2402.17699","paper":"/paper/gradient-based-discrete-sampling-with","title":"Gradient-based Discrete Sampling with Automatic Cyclical Scheduling","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"patrickpynadath1/automatic_cyclical_sampling","path":"mlp.py","file_url":"https://github.com/patrickpynadath1/automatic_cyclical_sampling/blob/HEAD/mlp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a01b6bf3d5c907c2","mcp_get_code":{"code_sha256":"a01b6bf3d5c907c2"}},{"arxiv_id":"2402.17414","paper":"/paper/neural-video-compression-with-feature","title":"Neural Video Compression with Feature Modulation","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/dcvc","path":"DCVC-family/DCVC-FM/src/models/video_model.py","file_url":"https://github.com/microsoft/dcvc/blob/HEAD/DCVC-family/DCVC-FM/src/models/video_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"194462a957fb0991","mcp_get_code":{"code_sha256":"194462a957fb0991"}},{"arxiv_id":"2402.17364","paper":"/paper/learning-dynamic-tetrahedra-for-high-quality","title":"Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzc21/DynTet","path":"data_utils/face_parsing/resnet.py","file_url":"https://github.com/zhangzc21/DynTet/blob/HEAD/data_utils/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.17318","paper":"/paper/scaling-supervised-local-learning-with","title":"Scaling Supervised Local Learning with Augmented Auxiliary Networks","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxiangma/auglocal","path":"networks/auxiliary_nets.py","file_url":"https://github.com/chenxiangma/auglocal/blob/HEAD/networks/auxiliary_nets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2402.15151","paper":"/paper/where-visual-speech-meets-language-vsp-llm","title":"Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing","date":"2024-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sally-sh/vsp-llm","path":"src/resnet.py","file_url":"https://github.com/sally-sh/vsp-llm/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2402.13578","paper":"/paper/transgop-transformer-based-gaze-object","title":"TransGOP: Transformer-Based Gaze Object Prediction","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenxi-Guo/TransGOP","path":"models/TransGOP/resnet.py","file_url":"https://github.com/chenxi-Guo/TransGOP/blob/HEAD/models/TransGOP/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2402.11846","paper":"/paper/unlearncanvas-a-stylized-image-dataset-to","title":"UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JingWu321/Scissorhands","path":"Classification/models/ResNet.py","file_url":"https://github.com/JingWu321/Scissorhands/blob/HEAD/Classification/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2402.11148","paper":"/paper/knowledge-distillation-based-on-transformed","title":"Knowledge Distillation Based on Transformed Teacher Matching","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkxufo/TTM","path":"models/resnet.py","file_url":"https://github.com/zkxufo/TTM/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.06244","paper":"/paper/quantifying-and-enhancing-multi-modal","title":"Quantifying and Enhancing Multi-modal Robustness with Modality Preference","date":"2024-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GeWu-Lab/Certifiable-Robust-Multi-modal-Training","path":"models/backbone.py","file_url":"https://github.com/GeWu-Lab/Certifiable-Robust-Multi-modal-Training/blob/HEAD/models/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2402.05453","paper":"/paper/mitigating-privacy-risk-in-membership","title":"Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-stat-Sustech/ConvexConcaveLoss","path":"source/models/resnet.py","file_url":"https://github.com/ml-stat-Sustech/ConvexConcaveLoss/blob/HEAD/source/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dec1b673b8a327b","mcp_get_code":{"code_sha256":"4dec1b673b8a327b"}},{"arxiv_id":"2402.03634","paper":"/paper/beam-beta-distribution-ray-denoising-for","title":"Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liewfeng/beam","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/liewfeng/beam/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2402.02263","paper":"/paper/mixednuts-training-free-accuracy-robustness","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Bai-YT/MixedNUTS","path":"models/base_models/bit_rn.py","file_url":"https://github.com/Bai-YT/MixedNUTS/blob/HEAD/models/base_models/bit_rn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2402.02009","paper":"/paper/robust-multi-task-learning-with-excess-risks","title":"Robust Multi-Task Learning with Excess Risks","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifei-he/ExcessMTL","path":"LibMTL/LibMTL/model/resnet.py","file_url":"https://github.com/yifei-he/ExcessMTL/blob/HEAD/LibMTL/LibMTL/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2402.01922","paper":"/paper/a-general-framework-for-learning-from-weak","title":"A General Framework for Learning from Weak Supervision","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hhhhhhao/general-framework-weak-supervision","path":"src/nets/preact_resnet.py","file_url":"https://github.com/hhhhhhao/general-framework-weak-supervision/blob/HEAD/src/nets/preact_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2402.01879","paper":"/paper/s-zero-gradient-based-optimization-of-ell-0","title":"$σ$-zero: Gradient-based Optimization of $\\ell_0$-norm Adversarial Examples","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cinofix/sigma-zero-adversarial-attack","path":"utils/resnet.py","file_url":"https://github.com/cinofix/sigma-zero-adversarial-attack/blob/HEAD/utils/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2402.01557","paper":"/paper/deep-continuous-networks","title":"Deep Continuous Networks","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.01399","paper":"/paper/a-probabilistic-model-to-explain-self","title":"A Probabilistic Model Behind Self-Supervised Learning","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alicebizeul/simvae","path":"src/model/resnet.py","file_url":"https://github.com/alicebizeul/simvae/blob/HEAD/src/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2402.01399","paper":"/paper/a-probabilistic-model-to-explain-self","title":"A Probabilistic Model Behind Self-Supervised Learning","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alicebizeul/simvae","path":"src/model/decoders.py","file_url":"https://github.com/alicebizeul/simvae/blob/HEAD/src/model/decoders.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"086e14bf480ea57e","mcp_get_code":{"code_sha256":"086e14bf480ea57e"}},{"arxiv_id":"2402.01123","paper":"/paper/a-single-simple-patch-is-all-you-need-for-ai","title":"A Single Simple Patch is All You Need for AI-generated Image Detection","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/SSP-AI-Generated-Image-Detection","path":"networks/resnet.py","file_url":"https://github.com/bcmi/SSP-AI-Generated-Image-Detection/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.00865","paper":"/paper/towards-optimal-feature-shaping-methods-for","title":"Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qinyu-allen-zhao/optfsood","path":"model/resnet.py","file_url":"https://github.com/qinyu-allen-zhao/optfsood/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2402.00627","paper":"/paper/caphuman-capture-your-moments-in-parallel","title":"CapHuman: Capture Your Moments in Parallel Universes","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vamosc/caphuman","path":"libs/face_parsing/resnet.py","file_url":"https://github.com/vamosc/caphuman/blob/HEAD/libs/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2402.00411","paper":"/paper/lm-ht-snn-enhancing-the-performance-of-snn-to","title":"LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzc1208/LMHT_SNN","path":"models/ResNet.py","file_url":"https://github.com/hzc1208/LMHT_SNN/blob/HEAD/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2402.03348","paper":"/paper/respect-the-model-fine-grained-and-robust-1","title":"Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sangyu-Han/SharingRatioDecomposition","path":"modules/resnet.py","file_url":"https://github.com/Sangyu-Han/SharingRatioDecomposition/blob/HEAD/modules/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e14e792923db61dd","mcp_get_code":{"code_sha256":"e14e792923db61dd"}},{"arxiv_id":"2401.16386","paper":"/paper/continual-learning-with-pre-trained-models-a","title":"Continual Learning with Pre-Trained Models: A Survey","date":"2024-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sun-hailong/LAMDA-PILOT","path":"backbone/resnet.py","file_url":"https://github.com/sun-hailong/LAMDA-PILOT/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2401.15883","paper":"/paper/transtroj-transferable-backdoor-attacks-to","title":"Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability","date":"2024-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haowang-cqu/transtroj","path":"models/resnet18.py","file_url":"https://github.com/haowang-cqu/transtroj/blob/HEAD/models/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2401.14846","paper":"/paper/understanding-domain-generalization-a-noise","title":"Understanding Domain Generalization: A Noise Robustness Perspective","date":"2024-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiaoruiyt/NoiseRobustDG","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/qiaoruiyt/NoiseRobustDG/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2401.13531","paper":"/paper/qagait-revisit-gait-recognition-from-a","title":"QAGait: Revisit Gait Recognition from a Quality Perspective","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wzb-bupt/QAGait","path":"opengait/modeling/modules.py","file_url":"https://github.com/wzb-bupt/QAGait/blob/HEAD/opengait/modeling/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2401.12689","paper":"/paper/energy-based-automated-model-evaluation","title":"Energy-based Automated Model Evaluation","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengr/energy_autoeval","path":"models/chenyaofo/resnet.py","file_url":"https://github.com/pengr/energy_autoeval/blob/HEAD/models/chenyaofo/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2401.12452","paper":"/paper/self-supervised-learning-of-lidar-3d-point","title":"Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rsy6318/CorrI2P","path":"imagenet.py","file_url":"https://github.com/rsy6318/CorrI2P/blob/HEAD/imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2401.11687","paper":"/paper/tim-an-efficient-temporal-interaction-module","title":"TIM: An Efficient Temporal Interaction Module for Spiking Transformer","date":"2024-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BrainCog-X/Brain-Cog","path":"braincog/model_zoo/resnet.py","file_url":"https://github.com/BrainCog-X/Brain-Cog/blob/HEAD/braincog/model_zoo/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"71d699d243383ea6","mcp_get_code":{"code_sha256":"71d699d243383ea6"}},{"arxiv_id":"2401.09257","paper":"/paper/a-first-order-multi-gradient-algorithm-for","title":"A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baijiong-lin/forum","path":"LibMTL/model/resnet.py","file_url":"https://github.com/baijiong-lin/forum/blob/HEAD/LibMTL/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2401.08501","paper":"/paper/values-a-framework-for-systematic-validation","title":"ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IML-DKFZ/values","path":"uncertainty_modeling/models/hrnet_module.py","file_url":"https://github.com/IML-DKFZ/values/blob/HEAD/uncertainty_modeling/models/hrnet_module.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2401.08407","paper":"/paper/cross-domain-few-shot-segmentation-via","title":"Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"niejiahao1998/ifa","path":"model/resnet.py","file_url":"https://github.com/niejiahao1998/ifa/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2401.07062","paper":"/paper/dirichlet-based-prediction-calibration-for","title":"Dirichlet-Based Prediction Calibration for Learning with Noisy Labels","date":"2024-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenchenzong/dpc","path":"AAAI2024_DPC_code/PreResNet.py","file_url":"https://github.com/chenchenzong/dpc/blob/HEAD/AAAI2024_DPC_code/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2401.06506","paper":"/paper/frequency-masking-for-universal-deepfake","title":"Frequency Masking for Universal Deepfake Detection","date":"2024-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chandlerbing65nm/FakeImageDetection","path":"networks/resnet.py","file_url":"https://github.com/chandlerbing65nm/FakeImageDetection/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2401.06187","paper":"/paper/scissorhands-scrub-data-influence-via","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingwu321/scissorhands","path":"Classification/models/ResNet.py","file_url":"https://github.com/jingwu321/scissorhands/blob/HEAD/Classification/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2401.03468","paper":"/paper/multichannel-av-wav2vec2-a-framework-for","title":"Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqs01/multi-channel-wav2vec2","path":"avhubert/resnet.py","file_url":"https://github.com/zqs01/multi-channel-wav2vec2/blob/HEAD/avhubert/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2401.02032","paper":"/paper/diffusionedge-diffusion-probabilistic-model","title":"DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuHuangAI/DiffusionEdge","path":"denoising_diffusion_pytorch/resnet.py","file_url":"https://github.com/GuHuangAI/DiffusionEdge/blob/HEAD/denoising_diffusion_pytorch/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2312.14404","paper":"/paper/cross-covariate-gait-recognition-a-benchmark","title":"Cross-Covariate Gait Recognition: A Benchmark","date":"2023-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shinanzou/ccgr","path":"opengait/modeling/modules.py","file_url":"https://github.com/shinanzou/ccgr/blob/HEAD/opengait/modeling/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.13131","paper":"/paper/scaling-compute-is-not-all-you-need-for","title":"Scaling Compute Is Not All You Need for Adversarial Robustness","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dedeswim/timm-adv-training","path":"src/models/adv_resnet.py","file_url":"https://github.com/dedeswim/timm-adv-training/blob/HEAD/src/models/adv_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.12703","paper":"/paper/federated-learning-with-extremely-noisy","title":"Federated Learning with Extremely Noisy Clients via Negative Distillation","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linchen99/fedned","path":"models/resnet.py","file_url":"https://github.com/linchen99/fedned/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2312.12703","paper":"/paper/federated-learning-with-extremely-noisy","title":"Federated Learning with Extremely Noisy Clients via Negative Distillation","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linchen99/fedned","path":"models/model_feature.py","file_url":"https://github.com/linchen99/fedned/blob/HEAD/models/model_feature.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2312.11536","paper":"/paper/fast-decision-boundary-based-out-of","title":"Fast Decision Boundary based Out-of-Distribution Detector","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"litianliu/fDBD-OOD","path":"models/resnet.py","file_url":"https://github.com/litianliu/fDBD-OOD/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2312.09997","paper":"/paper/one-self-configurable-model-to-solve-many","title":"One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mikomel/sal","path":"avr/model/copinet.py","file_url":"https://github.com/mikomel/sal/blob/HEAD/avr/model/copinet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9b80f52c276212a7","mcp_get_code":{"code_sha256":"9b80f52c276212a7"}},{"arxiv_id":"2312.09716","paper":"/paper/let-all-be-whitened-multi-teacher","title":"Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maryeon/whiten_mtd","path":"models/resnet.py","file_url":"https://github.com/maryeon/whiten_mtd/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2312.08977","paper":"/paper/weighted-ensemble-models-are-strong-continual","title":"Weighted Ensemble Models Are Strong Continual Learners","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iemprog/cofima","path":"convs/resnet.py","file_url":"https://github.com/iemprog/cofima/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.07806","paper":"/paper/contextually-affinitive-neighborhood-refinery-1","title":"Contextually Affinitive Neighborhood Refinery for Deep Clustering","date":"2023-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cly234/DeepClustering-ConNR","path":"network/preact_resnet.py","file_url":"https://github.com/cly234/DeepClustering-ConNR/blob/HEAD/network/preact_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2312.06372","paper":"/paper/ternary-spike-learning-ternary-spikes-for","title":"Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yfguo91/ternary-spike","path":"models/resnet.py","file_url":"https://github.com/yfguo91/ternary-spike/blob/HEAD/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dacc6812ec177540","mcp_get_code":{"code_sha256":"dacc6812ec177540"}},{"arxiv_id":"2312.06290","paper":"/paper/exploiting-label-skews-in-federated-learning","title":"Exploiting Label Skews in Federated Learning with Model Concatenation","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtudyq/fedconcat","path":"resnetcifar.py","file_url":"https://github.com/sjtudyq/fedconcat/blob/HEAD/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.05915","paper":"/paper/diffusion-for-natural-image-matting","title":"Diffusion for Natural Image Matting","date":"2023-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yihanhu-2022/diffmatte","path":"modeling/backbone/res34.py","file_url":"https://github.com/yihanhu-2022/diffmatte/blob/HEAD/modeling/backbone/res34.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.05385","paper":"/paper/apparate-rethinking-early-exits-to-tame","title":"Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving","date":"2023-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2312.04060","paper":"/paper/differentiable-registration-of-images-and-1","title":"Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junshengzhou/vp2p-match","path":"models/network_img/resnet.py","file_url":"https://github.com/junshengzhou/vp2p-match/blob/HEAD/models/network_img/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.03044","paper":"/paper/rest-enhancing-group-robustness-in-dnns","title":"REST: Enhancing Group Robustness in DNNs through Reweighted Sparse Training","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhao1402072392/rest","path":"extractable_resnet.py","file_url":"https://github.com/zhao1402072392/rest/blob/HEAD/extractable_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.02517","paper":"/paper/simplifying-neural-network-training-under-1","title":"Simplifying Neural Network Training Under Class Imbalance","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ravidziv/SimplifyingImbalancedTraining","path":"imbalanced/models/preresnet.py","file_url":"https://github.com/ravidziv/SimplifyingImbalancedTraining/blob/HEAD/imbalanced/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2312.02512","paper":"/paper/av2av-direct-audio-visual-speech-to-audio","title":"AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"choijeongsoo/av2av","path":"av2unit/avhubert/resnet.py","file_url":"https://github.com/choijeongsoo/av2av/blob/HEAD/av2unit/avhubert/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2312.00276","paper":"/paper/automating-continual-learning","title":"Automating Continual Learning","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idsia/automated-cl","path":"utils_other/resnet_dropblock_impl.py","file_url":"https://github.com/idsia/automated-cl/blob/HEAD/utils_other/resnet_dropblock_impl.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2312.00276","paper":"/paper/automating-continual-learning","title":"Automating Continual Learning","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idsia/automated-cl","path":"utils_other/resnet_impl.py","file_url":"https://github.com/idsia/automated-cl/blob/HEAD/utils_other/resnet_impl.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"2312.00206","paper":"/paper/sparsegs-real-time-360deg-sparse-view","title":"SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ForMyCat/SparseGS","path":"BoostingMonocularDepth/lib/Resnext_torch.py","file_url":"https://github.com/ForMyCat/SparseGS/blob/HEAD/BoostingMonocularDepth/lib/Resnext_torch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2312.00206","paper":"/paper/sparsegs-real-time-360deg-sparse-view","title":"SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ForMyCat/SparseGS","path":"BoostingMonocularDepth/lib/Resnet.py","file_url":"https://github.com/ForMyCat/SparseGS/blob/HEAD/BoostingMonocularDepth/lib/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.18649","paper":"/paper/simple-semantic-aided-few-shot-learning","title":"Simple Semantic-Aided Few-Shot Learning","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangdoudou123/semfew","path":"model/res12.py","file_url":"https://github.com/zhangdoudou123/semfew/blob/HEAD/model/res12.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.18649","paper":"/paper/simple-semantic-aided-few-shot-learning","title":"Simple Semantic-Aided Few-Shot Learning","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangdoudou123/semfew","path":"model/WRN28.py","file_url":"https://github.com/zhangdoudou123/semfew/blob/HEAD/model/WRN28.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2311.17626","paper":"/paper/focus-on-query-adversarial-mining-transformer-1","title":"Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wyxdm/AMNet","path":"model/resnet.py","file_url":"https://github.com/Wyxdm/AMNet/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.17590","paper":"/paper/synctalk-the-devil-is-in-the-synchronization","title":"SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZiqiaoPeng/SyncTalk","path":"data_utils/face_parsing/resnet.py","file_url":"https://github.com/ZiqiaoPeng/SyncTalk/blob/HEAD/data_utils/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.17232","paper":"/paper/reward-retinal-waves-for-pre-training","title":"ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bennyca/reward","path":"4_Visualization/nets/resnet.py","file_url":"https://github.com/bennyca/reward/blob/HEAD/4_Visualization/nets/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.17034","paper":"/paper/telling-left-from-right-identifying-geometry","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Junyi42/geoaware-sc","path":"model_utils/projection_network.py","file_url":"https://github.com/Junyi42/geoaware-sc/blob/HEAD/model_utils/projection_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2311.15502","paper":"/paper/learning-with-complementary-labels-revisited","title":"Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wwangwitsel/SCARCE","path":"cifar_models/resnet.py","file_url":"https://github.com/wwangwitsel/SCARCE/blob/HEAD/cifar_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.14975","paper":"/paper/eliminating-domain-bias-for-federated","title":"Eliminating Domain Bias for Federated Learning in Representation Space","date":"2023-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TsingZ0/DBE","path":"system/flcore/trainmodel/resnet.py","file_url":"https://github.com/TsingZ0/DBE/blob/HEAD/system/flcore/trainmodel/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2311.14905","paper":"/paper/class-gradient-projection-for-continual","title":"Class Gradient Projection For Continual Learning","date":"2023-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zackschen/CGP","path":"main_five_dataset.py","file_url":"https://github.com/zackschen/CGP/blob/HEAD/main_five_dataset.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2311.13613","paper":"/paper/spanning-training-progress-temporal-dual","title":"Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangxin-xd/Dataset-Pruning-TDDS","path":"models/resnet.py","file_url":"https://github.com/zhangxin-xd/Dataset-Pruning-TDDS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.13267","paper":"/paper/fedfn-feature-normalization-for-alleviating","title":"FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhoon-oh/FedBABU","path":"models/Nets.py","file_url":"https://github.com/jhoon-oh/FedBABU/blob/HEAD/models/Nets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7eccbc3004b9fde7","mcp_get_code":{"code_sha256":"7eccbc3004b9fde7"}},{"arxiv_id":"2311.11845","paper":"/paper/entangled-view-epipolar-information","title":"Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields","date":"2023-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tatakai1/evenerf","path":"evenerf/feature_network.py","file_url":"https://github.com/tatakai1/evenerf/blob/HEAD/evenerf/feature_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2311.10605","paper":"/paper/ca-jaccard-camera-aware-jaccard-distance-for","title":"CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification","date":"2023-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen960/ca-jaccard","path":"caj/models/resnet_ibn_a.py","file_url":"https://github.com/chen960/ca-jaccard/blob/HEAD/caj/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.10572","paper":"/paper/ssb-simple-but-strong-baseline-for-boosting-1","title":"SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning","date":"2023-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yue-fan/ssb","path":"models/resnet_imagenet.py","file_url":"https://github.com/yue-fan/ssb/blob/HEAD/models/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2311.07444","paper":"/paper/on-the-robustness-of-neural-collapse-and-the","title":"On the Robustness of Neural Collapse and the Neural Collapse of Robustness","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingtongsu/robust_neural_collapse","path":"preactresnet_imagenet.py","file_url":"https://github.com/jingtongsu/robust_neural_collapse/blob/HEAD/preactresnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.06750","paper":"/paper/federated-learning-for-generalization","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","date":"2023-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenkehuang/marsfl","path":"Backbones/ResNet_pretrain.py","file_url":"https://github.com/wenkehuang/marsfl/blob/HEAD/Backbones/ResNet_pretrain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe58fe326c4a2573","mcp_get_code":{"code_sha256":"fe58fe326c4a2573"}},{"arxiv_id":"2311.06750","paper":"/paper/federated-learning-for-generalization","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","date":"2023-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenkehuang/marsfl","path":"Backbones/ResNet.py","file_url":"https://github.com/wenkehuang/marsfl/blob/HEAD/Backbones/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2311.06443","paper":"/paper/cvthead-one-shot-controllable-head-avatar","title":"CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer","date":"2023-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"howiema/cvthead","path":"losses/vggface.py","file_url":"https://github.com/howiema/cvthead/blob/HEAD/losses/vggface.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2311.06423","paper":"/paper/flatness-aware-adversarial-attack","title":"Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness","date":"2023-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fmy266/TPA","path":"FAA/resnet.py","file_url":"https://github.com/fmy266/TPA/blob/HEAD/FAA/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2311.05152","paper":"/paper/cross-modal-prompts-adapting-large-pre","title":"Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoyi-duan/DG-SCT","path":"DG-SCT/AVE/nets/Resnet_VGGSound.py","file_url":"https://github.com/haoyi-duan/DG-SCT/blob/HEAD/DG-SCT/AVE/nets/Resnet_VGGSound.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2311.04251","paper":"/paper/mixturegrowth-growing-neural-networks-by","title":"MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaudatascience/mixturegrowth","path":"models/normal_wrn.py","file_url":"https://github.com/chaudatascience/mixturegrowth/blob/HEAD/models/normal_wrn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cbb2fbc77df061cb","mcp_get_code":{"code_sha256":"cbb2fbc77df061cb"}},{"arxiv_id":"2310.20332","paper":"/paper/recaptured-raw-screen-image-and-video-1","title":"Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tju-chengyijia/vd_raw","path":"model/MainNet.py","file_url":"https://github.com/tju-chengyijia/vd_raw/blob/HEAD/model/MainNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2310.19224","paper":"/paper/chammi-a-benchmark-for-channel-adaptive-1","title":"CHAMMI: A benchmark for channel-adaptive models in microscopy imaging","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaudatascience/channel_adaptive_models","path":"models/model_utils.py","file_url":"https://github.com/chaudatascience/channel_adaptive_models/blob/HEAD/models/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75d042751b02d666","mcp_get_code":{"code_sha256":"75d042751b02d666"}},{"arxiv_id":"2310.19182","paper":"/paper/fast-trainable-projection-for-robust-fine-1","title":"Fast Trainable Projection for Robust Fine-Tuning","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GT-RIPL/FTP","path":"models/resnet.py","file_url":"https://github.com/GT-RIPL/FTP/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.18574","paper":"/paper/breaking-the-trilemma-of-privacy-utility","title":"Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning","date":"2023-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guangyaodou/conmu","path":"cv_models/ResNet.py","file_url":"https://github.com/guangyaodou/conmu/blob/HEAD/cv_models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2310.18285","paper":"/paper/heterogeneous-federated-learning-with-group","title":"Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ubc-tea/SGPT","path":"models/modeling_resnet.py","file_url":"https://github.com/ubc-tea/SGPT/blob/HEAD/models/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2310.17645","paper":"/paper/defending-against-transfer-attacks-from","title":"PubDef: Defending Against Transfer Attacks From Public Models","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhengyuZhao/TransferAttackEval","path":"attacks/utils_iaa.py","file_url":"https://github.com/ZhengyuZhao/TransferAttackEval/blob/HEAD/attacks/utils_iaa.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.17622","paper":"/paper/combating-representation-learning-disparity-1","title":"Combating Representation Learning Disparity with Geometric Harmonization","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/Geometric-Harmonization","path":"resnet_imagenet.py","file_url":"https://github.com/MediaBrain-SJTU/Geometric-Harmonization/blob/HEAD/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2310.17622","paper":"/paper/combating-representation-learning-disparity-1","title":"Combating Representation Learning Disparity with Geometric Harmonization","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/Geometric-Harmonization","path":"SDCLR/resnet_prune.py","file_url":"https://github.com/MediaBrain-SJTU/Geometric-Harmonization/blob/HEAD/SDCLR/resnet_prune.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae216d6807b93383","mcp_get_code":{"code_sha256":"ae216d6807b93383"}},{"arxiv_id":"2310.15903","paper":"/paper/neural-collapse-in-multi-label-learning-with","title":"Neural Collapse in Multi-label Learning with Pick-all-label Loss","date":"2023-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"heimine/nc_mlab","path":"models/resnet.py","file_url":"https://github.com/heimine/nc_mlab/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.15712","paper":"/paper/gnesf-generalizable-neural-semantic-fields","title":"GNeSF: Generalizable Neural Semantic Fields","date":"2023-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HLinChen/GNeSF","path":"GNeSF-2D/gnesf/feature_network.py","file_url":"https://github.com/HLinChen/GNeSF/blob/HEAD/GNeSF-2D/gnesf/feature_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2310.15171","paper":"/paper/robodepth-robust-out-of-distribution-depth-1","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brandleyzhou/DIFFNet","path":"networks/CBAM_resnet.py","file_url":"https://github.com/brandleyzhou/DIFFNet/blob/HEAD/networks/CBAM_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2310.14227","paper":"/paper/revisiting-deep-ensemble-for-out-of","title":"Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective","date":"2023-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanghenshaometeor/ood-mode-ensemble","path":"model/cifar_wrn.py","file_url":"https://github.com/fanghenshaometeor/ood-mode-ensemble/blob/HEAD/model/cifar_wrn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2310.14226","paper":"/paper/multi-stream-cell-segmentation-with-low-level","title":"Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images","date":"2023-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lhaof/cellseg","path":"classifiers.py","file_url":"https://github.com/lhaof/cellseg/blob/HEAD/classifiers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.14019","paper":"/paper/you-only-condense-once-two-rules-for-pruning-1","title":"You Only Condense Once: Two Rules for Pruning Condensed Datasets","date":"2023-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-y/you-only-condense-once","path":"models/resnet.py","file_url":"https://github.com/he-y/you-only-condense-once/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2310.11341","paper":"/paper/dual-cognitive-architecture-incorporating","title":"Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/duca","path":"backbone/ResNet18.py","file_url":"https://github.com/neurai-lab/duca/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2310.11239","paper":"/paper/lidar-based-4d-occupancy-completion-and","title":"LiDAR-based 4D Occupancy Completion and Forecasting","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4ce/occ4cast","path":"baselines/models/conv2d.py","file_url":"https://github.com/ai4ce/occ4cast/blob/HEAD/baselines/models/conv2d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"caeaa72cca0b0383","mcp_get_code":{"code_sha256":"caeaa72cca0b0383"}},{"arxiv_id":"2310.11239","paper":"/paper/lidar-based-4d-occupancy-completion-and","title":"LiDAR-based 4D Occupancy Completion and Forecasting","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4ce/occ4cast","path":"baselines/models/conv3d.py","file_url":"https://github.com/ai4ce/occ4cast/blob/HEAD/baselines/models/conv3d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1fd61ab78733bff2","mcp_get_code":{"code_sha256":"1fd61ab78733bff2"}},{"arxiv_id":"2310.09192","paper":"/paper/does-graph-distillation-see-like-vision","title":"Does Graph Distillation See Like Vision Dataset Counterpart?","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RingBDStack/SGDD","path":"modules.py","file_url":"https://github.com/RingBDStack/SGDD/blob/HEAD/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"91525fcf771b6de5","mcp_get_code":{"code_sha256":"91525fcf771b6de5"}},{"arxiv_id":"2310.08855","paper":"/paper/overcoming-recency-bias-of-normalization-1","title":"Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lvyilin/AdaB2N","path":"backbone/ResNet18.py","file_url":"https://github.com/lvyilin/AdaB2N/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2310.08732","paper":"/paper/provably-robust-cost-sensitive-learning-via","title":"Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustmlrg/cs-rs","path":"archs/cifar_resnet.py","file_url":"https://github.com/trustmlrg/cs-rs/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2310.08732","paper":"/paper/provably-robust-cost-sensitive-learning-via","title":"Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustmlrg/cs-rs","path":"archs/ham_resnet.py","file_url":"https://github.com/trustmlrg/cs-rs/blob/HEAD/archs/ham_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2310.08117","paper":"/paper/dusa-decoupled-unsupervised-sim2real","title":"DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"refkxh/DUSA","path":"opencood/models/pixor.py","file_url":"https://github.com/refkxh/DUSA/blob/HEAD/opencood/models/pixor.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"2310.07855","paper":"/paper/cribo-self-supervised-learning-via-cross","title":"CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tileb1/CrIBo","path":"source/models/resnet.py","file_url":"https://github.com/tileb1/CrIBo/blob/HEAD/source/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2310.07716","paper":"/paper/pad-a-dataset-and-benchmark-for-pose-agnostic-1","title":"PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricLee0224/PAD","path":"models/backbones/resnet.py","file_url":"https://github.com/EricLee0224/PAD/blob/HEAD/models/backbones/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2db7a58e9288f8c7","mcp_get_code":{"code_sha256":"2db7a58e9288f8c7"}},{"arxiv_id":"2310.07587","paper":"/paper/fed-grab-federated-long-tailed-learning-with-1","title":"Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZackZikaiXiao/FedGraB","path":"model/model_res_official.py","file_url":"https://github.com/ZackZikaiXiao/FedGraB/blob/HEAD/model/model_res_official.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.07587","paper":"/paper/fed-grab-federated-long-tailed-learning-with-1","title":"Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZackZikaiXiao/FedGraB","path":"model/model_res.py","file_url":"https://github.com/ZackZikaiXiao/FedGraB/blob/HEAD/model/model_res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2310.06368","paper":"/paper/coinseg-contrast-inter-and-intra-class-1","title":"CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkzhang98/coinseg","path":"network/backbone/resnet.py","file_url":"https://github.com/zkzhang98/coinseg/blob/HEAD/network/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2310.05077","paper":"/paper/fedfed-feature-distillation-against-data-1","title":"FedFed: Feature Distillation against Data Heterogeneity in Federated Learning","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visitworld123/fedfed","path":"model/FL_VAE.py","file_url":"https://github.com/visitworld123/fedfed/blob/HEAD/model/FL_VAE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7d8ece6fefa8e53","mcp_get_code":{"code_sha256":"f7d8ece6fefa8e53"}},{"arxiv_id":"2310.04519","paper":"/paper/spade-sparsity-guided-debugging-for-deep","title":"SPADE: Sparsity-Guided Debugging for Deep Neural Networks","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ist-daslab/spade","path":"src/ResNet.py","file_url":"https://github.com/ist-daslab/spade/blob/HEAD/src/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.04519","paper":"/paper/spade-sparsity-guided-debugging-for-deep","title":"SPADE: Sparsity-Guided Debugging for Deep Neural Networks","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ist-daslab/spade","path":"src/LRP/resnet.py","file_url":"https://github.com/ist-daslab/spade/blob/HEAD/src/LRP/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c930c4c4b347fdeb","mcp_get_code":{"code_sha256":"c930c4c4b347fdeb"}},{"arxiv_id":"2310.04334","paper":"/paper/saliency-guided-hidden-associative-replay-for","title":"Saliency-Guided Hidden Associative Replay for Continual Learning","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baithebest/sharc","path":"backbone/ResNet18.py","file_url":"https://github.com/baithebest/sharc/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2310.02396","paper":"/paper/implicit-regularization-of-multi-task","title":"Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sflippl/multi-task","path":"functions/custom_resnet.py","file_url":"https://github.com/sflippl/multi-task/blob/HEAD/functions/custom_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2310.02206","paper":"/paper/chunking-forgetting-matters-in-continual","title":"Chunking: Continual Learning is not just about Distribution Shift","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tlee43/chunking-setting","path":"backbone/ResNet18.py","file_url":"https://github.com/tlee43/chunking-setting/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2310.01812","paper":"/paper/ppt-token-pruning-and-pooling-for-efficient","title":"PPT: Token Pruning and Pooling for Efficient Vision Transformers","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xjwu1024/PPT","path":"patchconvnet_models.py","file_url":"https://github.com/xjwu1024/PPT/blob/HEAD/patchconvnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ba6aa5f07daca9cd","mcp_get_code":{"code_sha256":"ba6aa5f07daca9cd"}},{"arxiv_id":"2310.01012","paper":"/paper/efficient-algorithms-for-the-cca-family","title":"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jameschapman19/ssl-ey","path":"resnet.py","file_url":"https://github.com/jameschapman19/ssl-ey/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2310.00527","paper":"/paper/self-supervised-learning-of-contextualized","title":"Self-supervised Learning of Contextualized Local Visual Embeddings","date":"2023-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sthalles/clove","path":"contrast/resnet.py","file_url":"https://github.com/sthalles/clove/blob/HEAD/contrast/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2310.00109","paper":"/paper/fedaiot-a-federated-learning-benchmark-for","title":"FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aiot-mlsys-lab/fedaiot","path":"models/epic_sounds.py","file_url":"https://github.com/aiot-mlsys-lab/fedaiot/blob/HEAD/models/epic_sounds.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2309.16286","paper":"/paper/generalizable-heterogeneous-federated-cross","title":"Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenkehuang/fccl","path":"fccl+/backbone/ResNet.py","file_url":"https://github.com/wenkehuang/fccl/blob/HEAD/fccl%2B/backbone/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2309.16108","paper":"/paper/channel-vision-transformers-an-image-is-worth","title":"Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"insitro/channelvit","path":"channelvit/backbone/fan.py","file_url":"https://github.com/insitro/channelvit/blob/HEAD/channelvit/backbone/fan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"53606449adac233f","mcp_get_code":{"code_sha256":"53606449adac233f"}},{"arxiv_id":"2309.14062","paper":"/paper/fecam-exploiting-the-heterogeneity-of-class-1","title":"FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dipamgoswami/fecam","path":"convs/resnet.py","file_url":"https://github.com/dipamgoswami/fecam/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2309.14062","paper":"/paper/fecam-exploiting-the-heterogeneity-of-class-1","title":"FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dipamgoswami/fecam","path":"convs/resnet_cbam.py","file_url":"https://github.com/dipamgoswami/fecam/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2309.14062","paper":"/paper/fecam-exploiting-the-heterogeneity-of-class-1","title":"FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dipamgoswami/fecam","path":"convs/modified_represnet.py","file_url":"https://github.com/dipamgoswami/fecam/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2309.12821","paper":"/paper/a-spectral-theory-of-neural-prediction-and","title":"A Spectral Theory of Neural Prediction and Alignment","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chung-neuroai-lab/snap","path":"snap/simclr/resnet.py","file_url":"https://github.com/chung-neuroai-lab/snap/blob/HEAD/snap/simclr/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2309.11160","paper":"/paper/multi-grained-temporal-prototype-learning-for","title":"Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation","date":"2023-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nankepan/VIPMT","path":"model/resnet.py","file_url":"https://github.com/nankepan/VIPMT/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2309.10255","paper":"/paper/rgb-based-category-level-object-pose","title":"RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goldoak/DMSR","path":"lib/pspnet.py","file_url":"https://github.com/goldoak/DMSR/blob/HEAD/lib/pspnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3fc43aa1154384a","mcp_get_code":{"code_sha256":"a3fc43aa1154384a"}},{"arxiv_id":"2309.07197","paper":"/paper/mitigating-adversarial-attacks-in-federated","title":"Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"queyrusi/pelta","path":"ExtendedPelta/Classes/TransformerResNet.py","file_url":"https://github.com/queyrusi/pelta/blob/HEAD/ExtendedPelta/Classes/TransformerResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2309.07197","paper":"/paper/mitigating-adversarial-attacks-in-federated","title":"Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"queyrusi/pelta","path":"ExtendedPelta/Classes/BigTransferModels.py","file_url":"https://github.com/queyrusi/pelta/blob/HEAD/ExtendedPelta/Classes/BigTransferModels.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0350ce32c8e146b","mcp_get_code":{"code_sha256":"b0350ce32c8e146b"}},{"arxiv_id":"2309.05994","paper":null,"title":"arXiv:2309.05994","date":null,"month_inferred_from_arxiv_id":"2023-09","title_source":null,"repo":"gaozhitong/ATTA","path":"lib/network/Resnet.py","file_url":"https://github.com/gaozhitong/ATTA/blob/HEAD/lib/network/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2309.05793","paper":"/paper/photoverse-tuning-free-image-customization","title":"PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idonahum/photoVerse","path":"models/arcface_resnet.py","file_url":"https://github.com/idonahum/photoVerse/blob/HEAD/models/arcface_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2309.05254","paper":"/paper/towards-better-data-exploitation-in-self","title":"Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuJF1226/BDEdepth","path":"networks/hrnet.py","file_url":"https://github.com/LiuJF1226/BDEdepth/blob/HEAD/networks/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2309.04803","paper":"/paper/towards-real-world-burst-image-super","title":"Towards Real-World Burst Image Super-Resolution: Benchmark and Method","date":"2023-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yjsunnn/fbanet","path":"model.py","file_url":"https://github.com/yjsunnn/fbanet/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2309.03903","paper":"/paper/tracking-anything-with-decoupled-video","title":"Tracking Anything with Decoupled Video Segmentation","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkchengrex/Tracking-Anything-with-DEVA","path":"deva/model/resnet.py","file_url":"https://github.com/hkchengrex/Tracking-Anything-with-DEVA/blob/HEAD/deva/model/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"2309.01488","paper":"/paper/on-the-use-of-mahalanobis-distance-for-out-of","title":"On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"harryanthony/mahalanobis-ood-detection","path":"source/models/wide_resnet.py","file_url":"https://github.com/harryanthony/mahalanobis-ood-detection/blob/HEAD/source/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2309.01429","paper":"/paper/adapting-segment-anything-model-for-change","title":"Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggsding/sam-cd","path":"models/SAM_CD.py","file_url":"https://github.com/ggsding/sam-cd/blob/HEAD/models/SAM_CD.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2309.01429","paper":"/paper/adapting-segment-anything-model-for-change","title":"Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggsding/sam-cd","path":"models/ResNet_CD.py","file_url":"https://github.com/ggsding/sam-cd/blob/HEAD/models/ResNet_CD.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5b0d5d7dc130f50","mcp_get_code":{"code_sha256":"c5b0d5d7dc130f50"}},{"arxiv_id":"2309.00399","paper":"/paper/fine-grained-recognition-with-learnable","title":"Fine-grained Recognition with Learnable Semantic Data Augmentation","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/LearnableISDA","path":"models/resnet.py","file_url":"https://github.com/LeapLabTHU/LearnableISDA/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2309.00081","paper":"/paper/few-shot-diagnosis-of-chest-x-rays-using-an","title":"Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces","date":"2023-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.15673","paper":"/paper/mdtd-a-multi-domain-trojan-detector-for-deep","title":"MDTD: A Multi Domain Trojan Detector for Deep Neural Networks","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajabia/mdtd","path":"Models.py","file_url":"https://github.com/rajabia/mdtd/blob/HEAD/Models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2308.14333","paper":"/paper/diffsmooth-certifiably-robust-learning-via","title":"DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"javyduck/diffsmooth","path":"archs/cifar_resnet.py","file_url":"https://github.com/javyduck/diffsmooth/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.13897","paper":"/paper/insertnerf-instilling-generalizability-into","title":"InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules","date":"2023-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbbbby-99/InsertNeRF","path":"network/ops.py","file_url":"https://github.com/bbbbby-99/InsertNeRF/blob/HEAD/network/ops.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2308.13772","paper":"/paper/boosting-residual-networks-with-group","title":"Boosting Residual Networks with Group Knowledge","date":"2023-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsj-001/aaai24-gkt","path":"gkt/model_bank/big_resnet.py","file_url":"https://github.com/tsj-001/aaai24-gkt/blob/HEAD/gkt/model_bank/big_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2308.13305","paper":"/paper/dynamic-residual-classifier-for-class","title":"Dynamic Residual Classifier for Class Incremental Learning","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen-xw/drc-cil","path":"models/mafdrc.py","file_url":"https://github.com/chen-xw/drc-cil/blob/HEAD/models/mafdrc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0295c5753083aba","mcp_get_code":{"code_sha256":"e0295c5753083aba"}},{"arxiv_id":"2308.13177","paper":"/paper/how-to-evaluate-the-generalization-of","title":"How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"om-ai-lab/OmDet","path":"omdet/modeling/backbone/dlafpn.py","file_url":"https://github.com/om-ai-lab/OmDet/blob/HEAD/omdet/modeling/backbone/dlafpn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2308.11937","paper":"/paper/learning-bottleneck-transformer-for-event","title":"Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"event-ahu/efv_event_classification","path":"model/torchvision_resnet.py","file_url":"https://github.com/event-ahu/efv_event_classification/blob/HEAD/model/torchvision_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2308.11796","paper":"/paper/time-does-tell-self-supervised-time-tuning-of","title":"Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smsd75/timetuning","path":"models.py","file_url":"https://github.com/smsd75/timetuning/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2308.11793","paper":"/paper/enhancing-nerf-akin-to-enhancing-llms","title":"Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/GNT-MOVE","path":"gnt/feature_network.py","file_url":"https://github.com/VITA-Group/GNT-MOVE/blob/HEAD/gnt/feature_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2308.11158","paper":"/paper/domain-generalization-via-rationale","title":"Domain Generalization via Rationale Invariance","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangchen527/ridg","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/liangchen527/ridg/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2308.11116","paper":"/paper/lan-hdr-luminance-based-alignment-network-for","title":"LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction","date":"2023-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haesoochung/lan-hdr","path":"models/layers.py","file_url":"https://github.com/haesoochung/lan-hdr/blob/HEAD/models/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef02d64125a1bf3d","mcp_get_code":{"code_sha256":"ef02d64125a1bf3d"}},{"arxiv_id":"2308.10603","paper":"/paper/a-step-towards-understanding-why","title":"A step towards understanding why classification helps regression","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"silvialaurapintea/reg-cls","path":"images/imdb-wiki-dir/resnet.py","file_url":"https://github.com/silvialaurapintea/reg-cls/blob/HEAD/images/imdb-wiki-dir/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.10445","paper":"/paper/when-prompt-based-incremental-learning-does","title":"When Prompt-based Incremental Learning Does Not Meet Strong Pretraining","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TOM-tym/APG","path":"inclearn/backbones/resnet.py","file_url":"https://github.com/TOM-tym/APG/blob/HEAD/inclearn/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.10239","paper":"/paper/from-global-to-local-multi-scale-out-of","title":"From Global to Local: Multi-scale Out-of-distribution Detection","date":"2023-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimzai/mode-ood","path":"models/resnet.py","file_url":"https://github.com/jimzai/mode-ood/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.10110","paper":"/paper/robust-mixture-of-expert-training-for","title":"Robust Mixture-of-Expert Training for Convolutional Neural Networks","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"optml-group/robust-moe-cnn","path":"models/resnet_imagenet_moe.py","file_url":"https://github.com/optml-group/robust-moe-cnn/blob/HEAD/models/resnet_imagenet_moe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eaf219fab088a30d","mcp_get_code":{"code_sha256":"eaf219fab088a30d"}},{"arxiv_id":"2308.09916","paper":"/paper/vi-net-boosting-category-level-6d-object-pose","title":"VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiehonglin/vi-net","path":"model/layer.py","file_url":"https://github.com/jiehonglin/vi-net/blob/HEAD/model/layer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b19355fc76b36c0","mcp_get_code":{"code_sha256":"3b19355fc76b36c0"}},{"arxiv_id":"2308.09318","paper":"/paper/towards-attack-tolerant-federated-learning","title":"Towards Attack-tolerant Federated Learning via Critical Parameter Analysis","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sungwon-Han/FEDCPA","path":"model.py","file_url":"https://github.com/Sungwon-Han/FEDCPA/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2308.09297","paper":"/paper/napa-vq-neighborhood-aware-prototype","title":"NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tamasham/napa-vq","path":"backbone/ResNet.py","file_url":"https://github.com/tamasham/napa-vq/blob/HEAD/backbone/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.09281","paper":"/paper/diverse-cotraining-makes-strong-semi","title":"Diverse Cotraining Makes Strong Semi-Supervised Segmentor","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"williamium3000/diverse-cotraining","path":"model/backbone/resnet.py","file_url":"https://github.com/williamium3000/diverse-cotraining/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2308.08327","paper":"/paper/adabrowse-adaptive-video-browser-for","title":"AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition","date":"2023-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hulianyuyy/adabrowse","path":"Stage_one/modules/resnet.py","file_url":"https://github.com/hulianyuyy/adabrowse/blob/HEAD/Stage_one/modules/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2308.07934","paper":"/paper/one-bit-flip-is-all-you-need-when-bit-flip","title":"One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training","date":"2023-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jianshuod/tba","path":"models/quan_resnet_imagenet.py","file_url":"https://github.com/jianshuod/tba/blob/HEAD/models/quan_resnet_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7fccbfe95159927d","mcp_get_code":{"code_sha256":"7fccbfe95159927d"}},{"arxiv_id":"2308.07686","paper":"/paper/boosting-multi-modal-model-performance-with","title":"Boosting Multi-modal Model Performance with Adaptive Gradient Modulation","date":"2023-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lihong2303/agm_iccv2023","path":"model/utils/resnet.py","file_url":"https://github.com/lihong2303/agm_iccv2023/blob/HEAD/model/utils/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2308.07163","paper":"/paper/hypersparse-neural-networks-shifting","title":"HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization","date":"2023-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"greenautoml4fas/hypersparse","path":"models/resnet.py","file_url":"https://github.com/greenautoml4fas/hypersparse/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.06735","paper":"/paper/aerialvln-vision-and-language-navigation-for","title":"AerialVLN: Vision-and-Language Navigation for UAVs","date":"2023-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AirVLN/AirVLN","path":"Model/utils/ddppo_resnet_utils.py","file_url":"https://github.com/AirVLN/AirVLN/blob/HEAD/Model/utils/ddppo_resnet_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b46c83f4e5438c40","mcp_get_code":{"code_sha256":"b46c83f4e5438c40"}},{"arxiv_id":"2308.06582","paper":"/paper/gated-attention-coding-for-training-high","title":"Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks","date":"2023-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bollossom/GAC","path":"CODE/CIFAR/models/MS_ResNet.py","file_url":"https://github.com/bollossom/GAC/blob/HEAD/CODE/CIFAR/models/MS_ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"71d699d243383ea6","mcp_get_code":{"code_sha256":"71d699d243383ea6"}},{"arxiv_id":"2308.06554","paper":"/paper/cyclic-test-time-adaptation-on-monocular","title":"Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction","date":"2023-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hygenie1228/cycleadapt_release","path":"lib/models/resnet.py","file_url":"https://github.com/hygenie1228/cycleadapt_release/blob/HEAD/lib/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.06248","paper":"/paper/funnybirds-a-synthetic-vision-dataset-for-a","title":"FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/funnybirds","path":"funnybirds_complete/models/resnet.py","file_url":"https://github.com/visinf/funnybirds/blob/HEAD/funnybirds_complete/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2308.06053","paper":"/paper/cost-effective-on-device-continual-learning","title":"Cost-effective On-device Continual Learning over Memory Hierarchy with Miro","date":"2023-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omnia-unist/Miro","path":"networks/der_resnet.py","file_url":"https://github.com/omnia-unist/Miro/blob/HEAD/networks/der_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2308.04995","paper":"/paper/idiff-face-synthetic-based-face-recognition","title":"IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fdbtrs/IDiff-Face","path":"face_recognition_training/backbones/iresnet.py","file_url":"https://github.com/fdbtrs/IDiff-Face/blob/HEAD/face_recognition_training/backbones/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2308.04995","paper":"/paper/idiff-face-synthetic-based-face-recognition","title":"IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fdbtrs/IDiff-Face","path":"face_recognition_training/backbones/utils.py","file_url":"https://github.com/fdbtrs/IDiff-Face/blob/HEAD/face_recognition_training/backbones/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"be7e40142c49c8be","mcp_get_code":{"code_sha256":"be7e40142c49c8be"}},{"arxiv_id":"2308.04650","paper":"/paper/deep-metric-learning-for-the-hemodynamics","title":"Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mandiehyewon/ssldml","path":"model/resnet.py","file_url":"https://github.com/mandiehyewon/ssldml/blob/HEAD/model/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8219daa4d25e40fa","mcp_get_code":{"code_sha256":"8219daa4d25e40fa"}},{"arxiv_id":"2308.03979","paper":"/paper/paif-perception-aware-infrared-visible-image","title":"PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuZhu-CV/BDLFusion","path":"model/backbone/resnet.py","file_url":"https://github.com/LiuZhu-CV/BDLFusion/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.03979","paper":"/paper/paif-perception-aware-infrared-visible-image","title":"PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuzhu-cv/crmef","path":"pietorch/se_nets.py","file_url":"https://github.com/liuzhu-cv/crmef/blob/HEAD/pietorch/se_nets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2308.03726","paper":"/paper/adaptivesam-towards-efficient-tuning-of-sam","title":"AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation","date":"2023-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jayparanjape/biastuning","path":"vit_seg_modeling_resnet_skip.py","file_url":"https://github.com/jayparanjape/biastuning/blob/HEAD/vit_seg_modeling_resnet_skip.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2308.03166","paper":"/paper/strategic-preys-make-acute-predators","title":"Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects","date":"2023-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChunmingHe/Camouflageator","path":"lib/resnet.py","file_url":"https://github.com/ChunmingHe/Camouflageator/blob/HEAD/lib/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2308.03003","paper":"/paper/cal-sfda-source-free-domain-adaptive-semantic","title":"Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error","date":"2023-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jo-wang/cal-sfda","path":"model/DeeplabV2.py","file_url":"https://github.com/jo-wang/cal-sfda/blob/HEAD/model/DeeplabV2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.02866","paper":"/paper/np-semiseg-when-neural-processes-meet-semi","title":"NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation","date":"2023-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jianf-wang/np-semiseg","path":"AugSeg/augseg/models/resnet.py","file_url":"https://github.com/jianf-wang/np-semiseg/blob/HEAD/AugSeg/augseg/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2308.01698","paper":"/paper/balanced-destruction-reconstruction-dynamics","title":"Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyuh/bdr-main","path":"networks/resnet18.py","file_url":"https://github.com/zyuh/bdr-main/blob/HEAD/networks/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.01686","paper":"/paper/lidar-camera-panoptic-segmentation-via","title":"LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzw12319/lcps","path":"network/swiftnet.py","file_url":"https://github.com/zhangzw12319/lcps/blob/HEAD/network/swiftnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2308.00376","paper":"/paper/deep-image-harmonization-with-learnable","title":"Deep Image Harmonization with Learnable Augmentation","date":"2023-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmi/SycoNet-Adaptive-Image-Harmonization","path":"models/modules.py","file_url":"https://github.com/bcmi/SycoNet-Adaptive-Image-Harmonization/blob/HEAD/models/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"78b2072ed1e3a34d","mcp_get_code":{"code_sha256":"78b2072ed1e3a34d"}},{"arxiv_id":"2307.16377","paper":"/paper/jotr-3d-joint-contrastive-learning-with","title":"JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery","date":"2023-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xljh0520/jotr","path":"common/nets/resnet.py","file_url":"https://github.com/xljh0520/jotr/blob/HEAD/common/nets/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.16184","paper":"/paper/unified-model-for-image-video-audio-and","title":"UnIVAL: Unified Model for Image, Video, Audio and Language Tasks","date":"2023-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mshukor/unival","path":"models/unival/resnet.py","file_url":"https://github.com/mshukor/unival/blob/HEAD/models/unival/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.14959","paper":"/paper/federated-model-aggregation-via-self","title":"Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmed-lab/fed-mas","path":"models/resnet.py","file_url":"https://github.com/xmed-lab/fed-mas/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.14959","paper":"/paper/federated-model-aggregation-via-self","title":"Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmed-lab/fed-mas","path":"models/resnet8.py","file_url":"https://github.com/xmed-lab/fed-mas/blob/HEAD/models/resnet8.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2307.14362","paper":"/paper/learnable-wavelet-neural-networks-for","title":"Learnable wavelet neural networks for cosmological inference","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Chris-Pedersen/LearnableWavelets","path":"learnable_wavelets/models/sn_top_models.py","file_url":"https://github.com/Chris-Pedersen/LearnableWavelets/blob/HEAD/learnable_wavelets/models/sn_top_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.12502","paper":"/paper/cross-contrastive-feature-perturbation-for","title":"Cross Contrasting Feature Perturbation for Domain Generalization","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hackmebroo/ccfp","path":"resnet_mixstyle.py","file_url":"https://github.com/hackmebroo/ccfp/blob/HEAD/resnet_mixstyle.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.10797","paper":"/paper/hyperreenact-one-shot-reenactment-via-jointly","title":"HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces","date":"2023-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stelabou/hyperreenact","path":"libs/face_models/fan_model/models.py","file_url":"https://github.com/stelabou/hyperreenact/blob/HEAD/libs/face_models/fan_model/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"2307.10404","paper":"/paper/interpreting-and-correcting-medical-image","title":"Interpreting and Correcting Medical Image Classification with PIP-Net","date":"2023-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"m-nauta/pipnet","path":"features/resnet_features.py","file_url":"https://github.com/m-nauta/pipnet/blob/HEAD/features/resnet_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.09749","paper":"/paper/towards-robust-scene-text-image-super","title":"Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement","date":"2023-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csguoh/LEMMA","path":"model/resnet.py","file_url":"https://github.com/csguoh/LEMMA/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.09323","paper":"/paper/efficient-region-aware-neural-radiance-fields","title":"Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fictionarry/er-nerf","path":"data_utils/face_parsing/resnet.py","file_url":"https://github.com/fictionarry/er-nerf/blob/HEAD/data_utils/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.08492","paper":"/paper/svdformer-complementing-point-cloud-via-self","title":"SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"czvvd/svdformer","path":"models/resnet.py","file_url":"https://github.com/czvvd/svdformer/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.08317","paper":"/paper/altfreezing-for-more-general-video-face-1","title":"AltFreezing for More General Video Face Forgery Detection","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhendongwang6/altfreezing","path":"model/classifier/_resnet_base.py","file_url":"https://github.com/zhendongwang6/altfreezing/blob/HEAD/model/classifier/_resnet_base.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.08187","paper":"/paper/an-empirical-investigation-of-pre-trained-1","title":"An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hiroki11x/timm_ood_calibration","path":"domainbed/networks.py","file_url":"https://github.com/hiroki11x/timm_ood_calibration/blob/HEAD/domainbed/networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.07245","paper":"/paper/freecos-self-supervised-learning-from","title":"FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation","date":"2023-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TY-Shi/FreeCOS","path":"base_model/hrnet.py","file_url":"https://github.com/TY-Shi/FreeCOS/blob/HEAD/base_model/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.06607","paper":"/paper/image-denoising-and-the-generative","title":"Image Denoising and the Generative Accumulation of Photons","date":"2023-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krulllab/gap","path":"gap/GAP_UNET_ResBlock.py","file_url":"https://github.com/krulllab/gap/blob/HEAD/gap/GAP_UNET_ResBlock.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe1bac001ffc64d5","mcp_get_code":{"code_sha256":"fe1bac001ffc64d5"}},{"arxiv_id":"2307.06099","paper":"/paper/rfenet-towards-reciprocal-feature-evolution","title":"RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation","date":"2023-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vankouf/rfenet","path":"network/resnext.py","file_url":"https://github.com/vankouf/rfenet/blob/HEAD/network/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.06099","paper":"/paper/rfenet-towards-reciprocal-feature-evolution","title":"RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation","date":"2023-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vankouf/rfenet","path":"network/resnet_d.py","file_url":"https://github.com/vankouf/rfenet/blob/HEAD/network/resnet_d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2307.04333","paper":"/paper/enhancing-adversarial-robustness-via-score-1","title":"Enhancing Adversarial Robustness via Score-Based Optimization","date":"2023-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzzhangboya/ScoreOpt","path":"clf_models/networks/wide_resnet.py","file_url":"https://github.com/zzzhangboya/ScoreOpt/blob/HEAD/clf_models/networks/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2307.04333","paper":"/paper/enhancing-adversarial-robustness-via-score-1","title":"Enhancing Adversarial Robustness via Score-Based Optimization","date":"2023-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzzhangboya/ScoreOpt","path":"clf_models/mnist.py","file_url":"https://github.com/zzzhangboya/ScoreOpt/blob/HEAD/clf_models/mnist.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a01b6bf3d5c907c2","mcp_get_code":{"code_sha256":"a01b6bf3d5c907c2"}},{"arxiv_id":"2307.03135","paper":"/paper/distilling-large-vision-language-model-with","title":"Distilling Large Vision-Language Model with Out-of-Distribution Generalizability","date":"2023-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuanlinli17/large_vlm_distillation_ood","path":"models/imagenet/resnet.py","file_url":"https://github.com/xuanlinli17/large_vlm_distillation_ood/blob/HEAD/models/imagenet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2307.02251","paper":"/paper/ranpac-random-projections-and-pre-trained","title":"RanPAC: Random Projections and Pre-trained Models for Continual Learning","date":"2023-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ranpac/ranpac","path":"resnet.py","file_url":"https://github.com/ranpac/ranpac/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2307.00198","paper":"/paper/filter-pruning-for-efficient-cnns-via","title":"Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler","date":"2023-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"osilly/kdfs","path":"model/pruned_model/resnet_pruned_cifar.py","file_url":"https://github.com/osilly/kdfs/blob/HEAD/model/pruned_model/resnet_pruned_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2306.16817","paper":"/paper/improving-online-continual-learning","title":"Improving Online Continual Learning Performance and Stability with Temporal Ensembles","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albinsou/online_ema","path":"toolkit/models.py","file_url":"https://github.com/albinsou/online_ema/blob/HEAD/toolkit/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2306.16817","paper":"/paper/improving-online-continual-learning","title":"Improving Online Continual Learning Performance and Stability with Temporal Ensembles","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albinsou/online_ema","path":"toolkit/resnet18.py","file_url":"https://github.com/albinsou/online_ema/blob/HEAD/toolkit/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec20f22a185cd708","mcp_get_code":{"code_sha256":"ec20f22a185cd708"}},{"arxiv_id":"2306.13856","paper":"/paper/learning-to-rank-meets-language-boosting-1","title":"Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification","date":"2023-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xk-huang/OrdinalCLIP","path":"ordinalclip/models/image_encoders/resnet.py","file_url":"https://github.com/xk-huang/OrdinalCLIP/blob/HEAD/ordinalclip/models/image_encoders/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2306.12685","paper":"/paper/rethinking-the-backward-propagation-for-1","title":"Rethinking the Backward Propagation for Adversarial Transferability","date":"2023-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Trustworthy-AI-Group/RPA","path":"models/ghost_resnet.py","file_url":"https://github.com/Trustworthy-AI-Group/RPA/blob/HEAD/models/ghost_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2306.12584","paper":"/paper/hierarchical-neural-simulation-based","title":"Hierarchical Neural Simulation-Based Inference Over Event Ensembles","date":"2023-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smsharma/hierarchical-inference","path":"models/resnet.py","file_url":"https://github.com/smsharma/hierarchical-inference/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2306.11074","paper":"/paper/simple-and-fast-group-robustness-by-automatic","title":"Simple and Fast Group Robustness by Automatic Feature Reweighting","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AndPotap/afr","path":"models/preresnet.py","file_url":"https://github.com/AndPotap/afr/blob/HEAD/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2306.06359","paper":"/paper/nerfool-uncovering-the-vulnerability-of","title":"NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations","date":"2023-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GATECH-EIC/NeRFool","path":"gnt/feature_network.py","file_url":"https://github.com/GATECH-EIC/NeRFool/blob/HEAD/gnt/feature_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e558f4229d053af","mcp_get_code":{"code_sha256":"7e558f4229d053af"}},{"arxiv_id":"2306.05957","paper":"/paper/ddlp-unsupervised-object-centric-video","title":"DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles","date":"2023-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taldatech/ddlp","path":"modules/modules.py","file_url":"https://github.com/taldatech/ddlp/blob/HEAD/modules/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00be6a9dc697991e","mcp_get_code":{"code_sha256":"00be6a9dc697991e"}},{"arxiv_id":"2306.05175","paper":"/paper/large-scale-dataset-pruning-with-dynamic","title":"Large-scale Dataset Pruning with Dynamic Uncertainty","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baai-dcai/dataset-pruning","path":"ImageNet/models/resnet.py","file_url":"https://github.com/baai-dcai/dataset-pruning/blob/HEAD/ImageNet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2306.03962","paper":"/paper/pillar-how-to-make-semi-private-learning-more","title":"PILLAR: How to make semi-private learning more effective","date":"2023-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FrancescoPinto/PILLAR","path":"resnet50_backbone.py","file_url":"https://github.com/FrancescoPinto/PILLAR/blob/HEAD/resnet50_backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2306.03726","paper":"/paper/exploring-model-dynamics-for-accumulative","title":"Exploring Model Dynamics for Accumulative Poisoning Discovery","date":"2023-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/memorization-discrepancy","path":"utils/model.py","file_url":"https://github.com/tmlr-group/memorization-discrepancy/blob/HEAD/utils/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2192bf2a0545102e","mcp_get_code":{"code_sha256":"2192bf2a0545102e"}},{"arxiv_id":"2306.02913","paper":"/paper/decentralized-sgd-and-average-direction-sam","title":"Decentralized SGD and Average-direction SAM are Asymptotically Equivalent","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raiden-zhu/icml-2023-dsgd-and-sam","path":"networks/resnet_micro.py","file_url":"https://github.com/raiden-zhu/icml-2023-dsgd-and-sam/blob/HEAD/networks/resnet_micro.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2306.02850","paper":"/paper/trace-5d-temporal-regression-of-avatars-with-1","title":"TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Arthur151/ROMP","path":"simple_romp/romp/model.py","file_url":"https://github.com/Arthur151/ROMP/blob/HEAD/simple_romp/romp/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2306.02416","paper":"/paper/training-like-a-medical-resident-universal","title":"Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhygao/UTNet","path":"model/conv_trans_utils.py","file_url":"https://github.com/yhygao/UTNet/blob/HEAD/model/conv_trans_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2306.02368","paper":"/paper/revisiting-data-free-knowledge-distillation","title":"Revisiting Data-Free Knowledge Distillation with Poisoned Teachers","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/abd","path":"zskt/models/torch_resnet.py","file_url":"https://github.com/illidanlab/abd/blob/HEAD/zskt/models/torch_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2306.02240","paper":"/paper/protect-prompt-tuning-for-hierarchical","title":"ProTeCt: Prompt Tuning for Taxonomic Open Set Classification","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gina9726/protect","path":"models/resnet.py","file_url":"https://github.com/gina9726/protect/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2306.02115","paper":"/paper/table-and-image-generation-for-investigating","title":"Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models","date":"2023-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OFA-Sys/OFA","path":"models/ofa/resnet.py","file_url":"https://github.com/OFA-Sys/OFA/blob/HEAD/models/ofa/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2306.02031","paper":"/paper/dos-diverse-outlier-sampling-for-out-of","title":"DOS: Diverse Outlier Sampling for Out-of-Distribution Detection","date":"2023-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lygjwy/DOS","path":"models/wide_resnet.py","file_url":"https://github.com/lygjwy/DOS/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2305.19533","paper":"/paper/dota-a-dynamically-operated-photonic-tensor","title":"Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator","date":null,"month_inferred_from_arxiv_id":"2023-05","title_source":"archive","repo":"scopex-asu/simphony","path":"models/quantized_BERT_base.py","file_url":"https://github.com/scopex-asu/simphony/blob/HEAD/models/quantized_BERT_base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10e0b0b4db69c475","mcp_get_code":{"code_sha256":"10e0b0b4db69c475"}},{"arxiv_id":"2305.19533","paper":"/paper/dota-a-dynamically-operated-photonic-tensor","title":"Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator","date":null,"month_inferred_from_arxiv_id":"2023-05","title_source":"archive","repo":"scopex-asu/simphony","path":"models/quantized_mobileViT.py","file_url":"https://github.com/scopex-asu/simphony/blob/HEAD/models/quantized_mobileViT.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323ca35cca0fe72e","mcp_get_code":{"code_sha256":"323ca35cca0fe72e"}},{"arxiv_id":"2305.18512","paper":"/paper/a-rainbow-in-deep-network-black-boxes","title":"A Rainbow in Deep Network Black Boxes","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"florentinguth/rainbow","path":"models/resnet.py","file_url":"https://github.com/florentinguth/rainbow/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2305.15862","paper":"/paper/a-task-guided-implicitly-searched-and-meta","title":"A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuzhu-cv/timfusion","path":"model/operations.py","file_url":"https://github.com/liuzhu-cv/timfusion/blob/HEAD/model/operations.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2305.13046","paper":"/paper/poem-polarization-of-embeddings-for-domain","title":"POEM: Polarization of Embeddings for Domain-Invariant Representations","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoSangYoung/Official-POEM","path":"POEM/domainbed/lib/wide_resnet.py","file_url":"https://github.com/JoSangYoung/Official-POEM/blob/HEAD/POEM/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2305.10808","paper":"/paper/manifold-aware-self-training-for-unsupervised","title":"Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gorilla-Lab-SCUT/MAST","path":"MAST/models/wide_resnet.py","file_url":"https://github.com/Gorilla-Lab-SCUT/MAST/blob/HEAD/MAST/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2305.10309","paper":"/paper/metamodulation-learning-variational-feature","title":"MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks","date":"2023-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmsdss/MetaModulation","path":"learner.py","file_url":"https://github.com/lmsdss/MetaModulation/blob/HEAD/learner.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2305.06968","paper":"/paper/humaniflow-ancestor-conditioned-normalising","title":"HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akashsengupta1997/humaniflow","path":"models/resnet.py","file_url":"https://github.com/akashsengupta1997/humaniflow/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2305.06968","paper":"/paper/humaniflow-ancestor-conditioned-normalising","title":"HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akashsengupta1997/humaniflow","path":"models/pose2D_hrnet.py","file_url":"https://github.com/akashsengupta1997/humaniflow/blob/HEAD/models/pose2D_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2305.06144","paper":"/paper/learning-semi-supervised-gaussian-mixture","title":"Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery","date":"2023-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DTennant/GPC","path":"models/wrn.py","file_url":"https://github.com/DTennant/GPC/blob/HEAD/models/wrn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2305.02507","paper":"/paper/stimulative-training-go-beyond-the","title":"Stimulative Training++: Go Beyond The Performance Limits of Residual Networks","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshine-ye/nips22-st","path":"models/pyramidnet_randwidth.py","file_url":"https://github.com/sunshine-ye/nips22-st/blob/HEAD/models/pyramidnet_randwidth.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2305.02507","paper":"/paper/stimulative-training-go-beyond-the","title":"Stimulative Training++: Go Beyond The Performance Limits of Residual Networks","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshine-ye/nips22-st","path":"models/resnet_randdepth.py","file_url":"https://github.com/sunshine-ye/nips22-st/blob/HEAD/models/resnet_randdepth.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2304.13098","paper":"/paper/uncovering-the-representation-of-spiking","title":"Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient","date":"2023-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"intelligent-computing-lab-yale/snncka","path":"models/ann_resnet.py","file_url":"https://github.com/intelligent-computing-lab-yale/snncka/blob/HEAD/models/ann_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76fdd94bb075ca26","mcp_get_code":{"code_sha256":"76fdd94bb075ca26"}},{"arxiv_id":"2304.10177","paper":"/paper/regularizing-second-order-influences-for","title":"Regularizing Second-Order Influences for Continual Learning","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feifeiobama/InfluenceCL","path":"backbone/ResNet18.py","file_url":"https://github.com/feifeiobama/InfluenceCL/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2304.09426","paper":"/paper/decoupled-training-for-long-tailed","title":"Decoupled Training for Long-Tailed Classification With Stochastic Representations","date":"2023-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhmiao/OpenLongTailRecognition-OLTR","path":"models/ResNetFeature.py","file_url":"https://github.com/zhmiao/OpenLongTailRecognition-OLTR/blob/HEAD/models/ResNetFeature.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2304.06024","paper":"/paper/probabilistic-human-mesh-recovery-in-3d","title":"Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views","date":"2023-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanweiliti/EgoHMR","path":"models/resnet.py","file_url":"https://github.com/sanweiliti/EgoHMR/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2304.04858","paper":"/paper/simulated-annealing-in-early-layers-leads-to","title":"Simulated Annealing in Early Layers Leads to Better Generalization","date":"2023-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"2304.03135","paper":"/paper/vlpd-context-aware-pedestrian-detection-via","title":"VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmy98129/VLPD","path":"lib/resnet.py","file_url":"https://github.com/lmy98129/VLPD/blob/HEAD/lib/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2304.03094","paper":"/paper/population-parameter-averaging-papa","title":"PopulAtion Parameter Averaging (PAPA)","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samsungsailmontreal/papa","path":"models/resnet.py","file_url":"https://github.com/samsungsailmontreal/papa/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ca893f9498143980","mcp_get_code":{"code_sha256":"ca893f9498143980"}},{"arxiv_id":"2304.02786","paper":"/paper/unicorn-a-unified-backdoor-trigger-inversion","title":"UNICORN: A Unified Backdoor Trigger Inversion Framework","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ru-system-software-and-security/unicorn","path":"models/resnet.py","file_url":"https://github.com/ru-system-software-and-security/unicorn/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2304.01973","paper":"/paper/erm-an-improved-baseline-for-domain","title":"ERM++: An Improved Baseline for Domain Generalization","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"piotr-teterwak/erm_plusplus","path":"models/resnet.py","file_url":"https://github.com/piotr-teterwak/erm_plusplus/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2304.01811","paper":"/paper/harsanyinet-computing-accurate-shapley-values","title":"HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csluchen/harsanyinet","path":"model/HarsanyiNet.py","file_url":"https://github.com/csluchen/harsanyinet/blob/HEAD/model/HarsanyiNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0da028b1b9234aa8","mcp_get_code":{"code_sha256":"0da028b1b9234aa8"}},{"arxiv_id":"2304.01054","paper":"/paper/voxelformer-bird-s-eye-view-feature","title":"VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection","date":"2023-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lizhuoling/voxelformer-public","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/lizhuoling/voxelformer-public/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2304.00933","paper":"/paper/knowledge-accumulation-in-continually-learned","title":"Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting","date":"2023-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timmhess/kaaff","path":"avalanche/models/icarl_resnet.py","file_url":"https://github.com/timmhess/kaaff/blob/HEAD/avalanche/models/icarl_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f522c857bf857a12","mcp_get_code":{"code_sha256":"f522c857bf857a12"}},{"arxiv_id":"2303.16947","paper":"/paper/de-coupling-and-de-positioning-dense-self","title":"De-coupling and De-positioning Dense Self-supervised Learning","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ztt1024/densessl","path":"analysis/imagenet_models/resnet.py","file_url":"https://github.com/ztt1024/densessl/blob/HEAD/analysis/imagenet_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.15555","paper":"/paper/object-discovery-from-motion-guided-tokens","title":"Object Discovery from Motion-Guided Tokens","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zpbao/MoTok","path":"models/model.py","file_url":"https://github.com/zpbao/MoTok/blob/HEAD/models/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e8e29138f60555f","mcp_get_code":{"code_sha256":"3e8e29138f60555f"}},{"arxiv_id":"2303.15409","paper":"/paper/classifier-robustness-enhancement-via-test","title":"Class-Conditioned Transformation for Enhanced Robust Image Classification","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsachiblau/Class-Conditioned-Transformation-for-Enhanced-Robust-Image-Classification","path":"model_arch/at_cifar.py","file_url":"https://github.com/tsachiblau/Class-Conditioned-Transformation-for-Enhanced-Robust-Image-Classification/blob/HEAD/model_arch/at_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2303.15409","paper":"/paper/classifier-robustness-enhancement-via-test","title":"Class-Conditioned Transformation for Enhanced Robust Image Classification","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsachiblau/Class-Conditioned-Transformation-for-Enhanced-Robust-Image-Classification","path":"model_arch/wrn_28_10.py","file_url":"https://github.com/tsachiblau/Class-Conditioned-Transformation-for-Enhanced-Robust-Image-Classification/blob/HEAD/model_arch/wrn_28_10.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2303.15269","paper":"/paper/handwritten-text-generation-from-visual","title":"Handwritten Text Generation from Visual Archetypes","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hnam-1765/writevit","path":"models/backbone.py","file_url":"https://github.com/hnam-1765/writevit/blob/HEAD/models/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2303.14978","paper":"/paper/learned-image-compression-with-mixed","title":"Learned Image Compression with Mixed Transformer-CNN Architectures","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nikolai10/LIC-TCM","path":"arch_ops.py","file_url":"https://github.com/Nikolai10/LIC-TCM/blob/HEAD/arch_ops.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c2107acec370b728","mcp_get_code":{"code_sha256":"c2107acec370b728"}},{"arxiv_id":"2303.14531","paper":"/paper/sio-synthetic-in-distribution-data-benefits","title":"SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjysteven/sio","path":"openood/networks/bit.py","file_url":"https://github.com/zjysteven/sio/blob/HEAD/openood/networks/bit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2303.13995","paper":"/paper/line-out-of-distribution-detection-by","title":"LINe: Out-of-Distribution Detection by Leveraging Important Neurons","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons","path":"models/resnet.py","file_url":"https://github.com/yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.13995","paper":"/paper/line-out-of-distribution-detection-by","title":"LINe: Out-of-Distribution Detection by Leveraging Important Neurons","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons","path":"models/resnetv2.py","file_url":"https://github.com/yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons/blob/HEAD/models/resnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2303.13839","paper":"/paper/hrdoc-dataset-and-baseline-method-toward","title":"HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jfma-USTC/HRDoc","path":"end2end_system/strcut_recover/libs/model/encoder.py","file_url":"https://github.com/jfma-USTC/HRDoc/blob/HEAD/end2end_system/strcut_recover/libs/model/encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"49abff9073550b42","mcp_get_code":{"code_sha256":"49abff9073550b42"}},{"arxiv_id":"2303.11932","paper":"/paper/using-explanations-to-guide-models","title":"Studying How to Efficiently and Effectively Guide Models with Explanations","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sukrutrao/Model-Guidance","path":"fixup_resnet.py","file_url":"https://github.com/sukrutrao/Model-Guidance/blob/HEAD/fixup_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.11906","paper":"/paper/solving-oscillation-problem-in-post-training","title":"Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/mrecg","path":"models/resnet.py","file_url":"https://github.com/bytedance/mrecg/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.10353","paper":"/paper/sharpness-aware-gradient-matching-for-domain","title":"Sharpness-Aware Gradient Matching for Domain Generalization","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2303.08384","paper":"/paper/rethinking-optical-flow-from-geometric","title":"Rethinking Optical Flow from Geometric Matching Consistent Perspective","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DQiaole/MatchFlow","path":"core/resnet_fpn.py","file_url":"https://github.com/DQiaole/MatchFlow/blob/HEAD/core/resnet_fpn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.06870","paper":"/paper/three-guidelines-you-should-know-for","title":"Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/us3l-cvpr2023","path":"models/resnet.py","file_url":"https://github.com/megvii-research/us3l-cvpr2023/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2303.05938","paper":"/paper/acr-attention-collaboration-based-regressor","title":"ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction","date":"2023-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengdiyu/arbitrary-hands-3d-reconstruction","path":"acr/model.py","file_url":"https://github.com/zhengdiyu/arbitrary-hands-3d-reconstruction/blob/HEAD/acr/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.05148","paper":"/paper/weakly-supervised-knowledge-transfer-with","title":"Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"molden/ProbKT","path":"robust_detection/baselines/cnn_model.py","file_url":"https://github.com/molden/ProbKT/blob/HEAD/robust_detection/baselines/cnn_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.04995","paper":"/paper/text-visual-prompting-for-efficient-2d","title":"Text-Visual Prompting for Efficient 2D Temporal Video Grounding","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"intel/TVP","path":"src/modeling/grid_feat.py","file_url":"https://github.com/intel/TVP/blob/HEAD/src/modeling/grid_feat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"17e89eef72e37efe","mcp_get_code":{"code_sha256":"17e89eef72e37efe"}},{"arxiv_id":"2303.04249","paper":"/paper/where-we-are-and-what-we-re-looking-at-query","title":"Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes","date":"2023-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2303.00332","paper":"/paper/cam-a-fast-and-efficient-network-for-speaker","title":"CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"wenet-e2e/wespeaker","path":"wespeaker/models/eres2net.py","file_url":"https://github.com/wenet-e2e/wespeaker/blob/HEAD/wespeaker/models/eres2net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2302.14685","paper":"/paper/dart-diversify-aggregate-repeat-training","title":"DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks","date":"2023-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"val-iisc/DART","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/val-iisc/DART/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2302.13130","paper":"/paper/point-cloud-forecasting-as-a-proxy-for-4d","title":"Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting","date":"2023-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tarashakhurana/4d-occ-forecasting","path":"model.py","file_url":"https://github.com/tarashakhurana/4d-occ-forecasting/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"caeaa72cca0b0383","mcp_get_code":{"code_sha256":"caeaa72cca0b0383"}},{"arxiv_id":"2302.12480","paper":"/paper/robust-weight-signatures-gaining-robustness","title":"Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?","date":"2023-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/Robust_Weight_Signatures","path":"models/resnet.py","file_url":"https://github.com/VITA-Group/Robust_Weight_Signatures/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2302.12288","paper":"/paper/zoedepth-zero-shot-transfer-by-combining","title":"ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thygate/stable-diffusion-webui-depthmap-script","path":"lib/Resnext_torch.py","file_url":"https://github.com/thygate/stable-diffusion-webui-depthmap-script/blob/HEAD/lib/Resnext_torch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2302.12288","paper":"/paper/zoedepth-zero-shot-transfer-by-combining","title":"ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thygate/stable-diffusion-webui-depthmap-script","path":"lib/Resnet.py","file_url":"https://github.com/thygate/stable-diffusion-webui-depthmap-script/blob/HEAD/lib/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2302.11984","paper":"/paper/unsupervised-domain-adaptation-via-distilled","title":"Unsupervised Domain Adaptation via Distilled Discriminative Clustering","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huitangtang/disclusterda","path":"models/resnet.py","file_url":"https://github.com/huitangtang/disclusterda/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2302.10912","paper":"/paper/balanced-audiovisual-dataset-for-imbalance","title":"Balanced Audiovisual Dataset for Imbalance Analysis","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2302.10174","paper":"/paper/towards-universal-fake-image-detectors-that","title":"Towards Universal Fake Image Detectors that Generalize Across Generative Models","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuheng-li/universalfakedetect","path":"models/resnet.py","file_url":"https://github.com/yuheng-li/universalfakedetect/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2302.10174","paper":"/paper/towards-universal-fake-image-detectors-that","title":"Towards Universal Fake Image Detectors that Generalize Across Generative Models","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuheng-li/universalfakedetect","path":"networks/resnet_lpf.py","file_url":"https://github.com/yuheng-li/universalfakedetect/blob/HEAD/networks/resnet_lpf.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"2302.08769","paper":"/paper/collaborative-discrepancy-optimization-for-1","title":"Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caoyunkang/CDO","path":"model/cdo.py","file_url":"https://github.com/caoyunkang/CDO/blob/HEAD/model/cdo.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2302.01190","paper":"/paper/on-the-efficacy-of-differentially-private-few","title":"On the Efficacy of Differentially Private Few-shot Image Classification","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/dp-few-shot","path":"src/bit_resnet.py","file_url":"https://github.com/cambridge-mlg/dp-few-shot/blob/HEAD/src/bit_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2302.01190","paper":"/paper/on-the-efficacy-of-differentially-private-few","title":"On the Efficacy of Differentially Private Few-shot Image Classification","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/dp-few-shot","path":"ml-flair/flair_model.py","file_url":"https://github.com/cambridge-mlg/dp-few-shot/blob/HEAD/ml-flair/flair_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9ccdf59f83eb0db1","mcp_get_code":{"code_sha256":"9ccdf59f83eb0db1"}},{"arxiv_id":"2301.13430","paper":"/paper/geneface-generalized-and-high-fidelity-audio","title":"GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yerfor/geneface","path":"data_util/face_parsing/resnet.py","file_url":"https://github.com/yerfor/geneface/blob/HEAD/data_util/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2301.11929","paper":"/paper/training-full-spike-neural-networks-via","title":"Training Full Spike Neural Networks via Auxiliary Accumulation Pathway","date":"2023-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iCGY96/AAP","path":"imagenet/dsnn.py","file_url":"https://github.com/iCGY96/AAP/blob/HEAD/imagenet/dsnn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2301.11578","paper":"/paper/learning-to-unlearn-instance-wise-unlearning","title":"Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers","date":"2023-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csm9493/L2UL","path":"resnet.py","file_url":"https://github.com/csm9493/L2UL/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2301.10908","paper":"/paper/distilling-cognitive-backdoor-patterns-within","title":"Distilling Cognitive Backdoor Patterns within an Image","date":"2023-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HanxunH/CognitiveDistillation","path":"models/celeba_resnet.py","file_url":"https://github.com/HanxunH/CognitiveDistillation/blob/HEAD/models/celeba_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2301.09879","paper":"/paper/data-augmentation-alone-can-improve","title":"Data Augmentation Alone Can Improve Adversarial Training","date":"2023-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"treelli/da-alone-improves-at","path":"src/model/wide_resnet.py","file_url":"https://github.com/treelli/da-alone-improves-at/blob/HEAD/src/model/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2301.07635","paper":"/paper/local-learning-with-neuron-groups","title":"Local Learning with Neuron Groups","date":"2023-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adeetyapatel12/gn-dgl","path":"Multilayer_GN_DGL/networks/resnet.py","file_url":"https://github.com/adeetyapatel12/gn-dgl/blob/HEAD/Multilayer_GN_DGL/networks/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"73e3dd93c47e13dd","mcp_get_code":{"code_sha256":"73e3dd93c47e13dd"}},{"arxiv_id":"2301.07340","paper":"/paper/semi-supervised-semantic-segmentation-via-4","title":"Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant","date":"2023-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jin-Ying/GTA-Seg","path":"gta/models/resnet.py","file_url":"https://github.com/Jin-Ying/GTA-Seg/blob/HEAD/gta/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2301.06442","paper":"/paper/modeling-uncertain-feature-representation-for","title":"Modeling Uncertain Feature Representation for Domain Generalization","date":"2023-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lixiaotong97/DSU","path":"dsu.py","file_url":"https://github.com/lixiaotong97/DSU/blob/HEAD/dsu.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2301.06442","paper":"/paper/modeling-uncertain-feature-representation-for","title":"Modeling Uncertain Feature Representation for Domain Generalization","date":"2023-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"db16069021194d10","mcp_get_code":{"code_sha256":"db16069021194d10"}},{"arxiv_id":"2301.01197","paper":"/paper/backdoor-attacks-against-dataset-distillation","title":"Backdoor Attacks Against Dataset Distillation","date":"2023-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyugeng/baadd","path":"DD/networks/ResNet.py","file_url":"https://github.com/liuyugeng/baadd/blob/HEAD/DD/networks/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2301.01006","paper":"/paper/policy-pre-training-for-end-to-end-autonomous","title":"Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling","date":"2023-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opendrivelab/ppgeo","path":"resnet.py","file_url":"https://github.com/opendrivelab/ppgeo/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2212.11702","paper":"/paper/robust-meta-representation-learning-via","title":"Robust Meta-Representation Learning via Global Label Inference and Classification","date":"2022-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isakfalk/mela","path":"models/resnet.py","file_url":"https://github.com/isakfalk/mela/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2212.11684","paper":"/paper/scene-aware-egocentric-3d-human-pose","title":"Scene-aware Egocentric 3D Human Pose Estimation","date":"2022-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2212.09000","paper":"/paper/confidence-aware-training-of-smoothed-1","title":"Confidence-aware Training of Smoothed Classifiers for Certified Robustness","date":"2022-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alinlab/smoothing-catrs","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/alinlab/smoothing-catrs/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2212.08570","paper":"/paper/audio-based-ai-classifiers-show-no-evidence","title":"Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alan-turing-institute/turing-rss-health-data-lab-biomedical-acoustic-markers","path":"BNNBaseline/lib/ResNetSource.py","file_url":"https://github.com/alan-turing-institute/turing-rss-health-data-lab-biomedical-acoustic-markers/blob/HEAD/BNNBaseline/lib/ResNetSource.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2212.07101","paper":"/paper/domain-generalization-by-learning-and","title":"Domain Generalization by Learning and Removing Domain-specific Features","date":"2022-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yulearningg/LRDG","path":"models/nns/resnet.py","file_url":"https://github.com/yulearningg/LRDG/blob/HEAD/models/nns/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2212.06079","paper":"/paper/robust-perception-through-equivariance","title":"Robust Perception through Equivariance","date":"2022-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nayeemrizve/invariance-equivariance","path":"models/resnet_inv_eq.py","file_url":"https://github.com/nayeemrizve/invariance-equivariance/blob/HEAD/models/resnet_inv_eq.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a199e6993774728a","mcp_get_code":{"code_sha256":"a199e6993774728a"}},{"arxiv_id":"2212.05245","paper":"/paper/joint-spatio-temporal-modeling-for-semantic","title":"Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images","date":"2022-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2212.04985","paper":"/paper/understanding-and-combating-robust","title":"Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization","date":"2022-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"treelli/combating-ro-advlc","path":"src/model/wide_resnet.py","file_url":"https://github.com/treelli/combating-ro-advlc/blob/HEAD/src/model/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2212.02191","paper":"/paper/partial-variance-reduction-improves-non","title":"On the effectiveness of partial variance reduction in federated learning with heterogeneous data","date":"2022-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyn1874/fedssyn","path":"fed_model/resnet.py","file_url":"https://github.com/lyn1874/fedssyn/blob/HEAD/fed_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2212.01927","paper":"/paper/label-encoding-for-regression-networks-1","title":"Label Encoding for Regression Networks","date":"2022-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ubc-aamodt-group/bel_regression","path":"facial_detection/direct_regression/lib/models/hrnet.py","file_url":"https://github.com/ubc-aamodt-group/bel_regression/blob/HEAD/facial_detection/direct_regression/lib/models/hrnet.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"22e4091386278e07","mcp_get_code":{"code_sha256":"22e4091386278e07"}},{"arxiv_id":"2211.16231","paper":"/paper/curriculum-temperature-for-knowledge","title":"Curriculum Temperature for Knowledge Distillation","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengli97/ctkd","path":"models/resnetv2.py","file_url":"https://github.com/zhengli97/ctkd/blob/HEAD/models/resnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2211.16231","paper":"/paper/curriculum-temperature-for-knowledge","title":"Curriculum Temperature for Knowledge Distillation","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengli97/ctkd","path":"models/resnet.py","file_url":"https://github.com/zhengli97/ctkd/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.15059","paper":"/paper/learning-dense-object-descriptors-from","title":"Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization","date":"2022-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rehg-lab/dope_selfsup","path":"dope_selfsup/nets/resnet.py","file_url":"https://github.com/rehg-lab/dope_selfsup/blob/HEAD/dope_selfsup/nets/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.14758","paper":"/paper/videoretalking-audio-based-lip","title":"VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild","date":"2022-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinthony/video-retalking","path":"third_part/face_detection/models.py","file_url":"https://github.com/vinthony/video-retalking/blob/HEAD/third_part/face_detection/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"2211.14666","paper":"/paper/synergies-between-disentanglement-and","title":"Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tristandeleu/synergies-disentanglement-sparsity","path":"metaoptnet/models/ResNet12_embedding.py","file_url":"https://github.com/tristandeleu/synergies-disentanglement-sparsity/blob/HEAD/metaoptnet/models/ResNet12_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.14512","paper":"/paper/residual-pattern-learning-for-pixel-wise-out","title":"Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yyliu01/rpl","path":"rpl.code/model/resnet.py","file_url":"https://github.com/yyliu01/rpl/blob/HEAD/rpl.code/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.13264","paper":"/paper/distilling-knowledge-from-self-supervised","title":"Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yccm/ega","path":"models/resnet.py","file_url":"https://github.com/yccm/ega/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.12933","paper":"/paper/join-the-high-accuracy-club-on-imagenet-with","title":"Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hpi-xnor/bnext","path":"src/bnext.py","file_url":"https://github.com/hpi-xnor/bnext/blob/HEAD/src/bnext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"71541b9507f823af","mcp_get_code":{"code_sha256":"71541b9507f823af"}},{"arxiv_id":"2211.12368","paper":"/paper/real-time-neural-radiance-talking-portrait","title":"Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition","date":"2022-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashawkey/RAD-NeRF","path":"data_utils/face_parsing/resnet.py","file_url":"https://github.com/ashawkey/RAD-NeRF/blob/HEAD/data_utils/face_parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.11514","paper":"/paper/prosfda-prompt-learning-based-source-free","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shishuaihu/prosfda","path":"prosfda/models/resnet.py","file_url":"https://github.com/shishuaihu/prosfda/blob/HEAD/prosfda/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.11208","paper":"/paper/next3d-generative-neural-texture","title":"Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MrTornado24/FENeRF","path":"generators/encoder_model.py","file_url":"https://github.com/MrTornado24/FENeRF/blob/HEAD/generators/encoder_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"2211.10831","paper":"/paper/joint-embedding-predictive-architectures","title":"Joint Embedding Predictive Architectures Focus on Slow Features","date":"2022-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vladisai/jepa_ssl_neurips_2022","path":"resnet.py","file_url":"https://github.com/vladisai/jepa_ssl_neurips_2022/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2211.08583","paper":"/paper/empirical-study-on-optimizer-selection-for","title":"Empirical Study on Optimizer Selection for Out-of-Distribution Generalization","date":"2022-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hiroki11x/optimizer_comparison_ood","path":"backgrounds_challenge/imagenet_models/leaky_resnet.py","file_url":"https://github.com/hiroki11x/optimizer_comparison_ood/blob/HEAD/backgrounds_challenge/imagenet_models/leaky_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.08326","paper":"/paper/contrastive-learning-for-regression-in-multi","title":"Contrastive learning for regression in multi-site brain age prediction","date":"2022-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EIDOSLAB/contrastive-brain-age-prediction","path":"ramp-submission/estimator.py","file_url":"https://github.com/EIDOSLAB/contrastive-brain-age-prediction/blob/HEAD/ramp-submission/estimator.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68212b2060dc618d","mcp_get_code":{"code_sha256":"68212b2060dc618d"}},{"arxiv_id":"2211.08044","paper":"/paper/backdoor-attacks-for-remote-sensing-data-with","title":"Backdoor Attacks for Remote Sensing Data with Wavelet Transform","date":"2022-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ndraeger/waba","path":"classification/models/networks.py","file_url":"https://github.com/ndraeger/waba/blob/HEAD/classification/models/networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e98e1d315e2ea27","mcp_get_code":{"code_sha256":"7e98e1d315e2ea27"}},{"arxiv_id":"2211.05109","paper":"/paper/vitality-unifying-low-rank-and-sparse","title":"ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention","date":"2022-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GATECH-EIC/ViTaLiTy","path":"src/patchconvnet_models.py","file_url":"https://github.com/GATECH-EIC/ViTaLiTy/blob/HEAD/src/patchconvnet_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"e90596193a6f191a","mcp_get_code":{"code_sha256":"e90596193a6f191a"}},{"arxiv_id":"2211.04031","paper":"/paper/hilbert-distillation-for-cross-dimensionality","title":"Hilbert Distillation for Cross-Dimensionality Networks","date":"2022-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EagleMIT/Hilbert-Distillation","path":"model/resnet_2d.py","file_url":"https://github.com/EagleMIT/Hilbert-Distillation/blob/HEAD/model/resnet_2d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2211.00680","paper":"/paper/on-the-detection-of-synthetic-images","title":"On the detection of synthetic images generated by diffusion models","date":"2022-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grip-unina/dmimagedetection","path":"test_code/networks/resnet.py","file_url":"https://github.com/grip-unina/dmimagedetection/blob/HEAD/test_code/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2211.00680","paper":"/paper/on-the-detection-of-synthetic-images","title":"On the detection of synthetic images generated by diffusion models","date":"2022-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grip-unina/dmimagedetection","path":"test_code/networks/resnet_mod.py","file_url":"https://github.com/grip-unina/dmimagedetection/blob/HEAD/test_code/networks/resnet_mod.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"67c9ab8e625b4e5f","mcp_get_code":{"code_sha256":"67c9ab8e625b4e5f"}},{"arxiv_id":"2210.16765","paper":"/paper/benchmarking-adversarial-patch-against-aerial","title":"Benchmarking Adversarial Patch Against Aerial Detection","date":"2022-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaweilian/ap-pa","path":"models/resnet.py","file_url":"https://github.com/jiaweilian/ap-pa/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2210.15127","paper":"/paper/rethinking-the-reverse-engineering-of-trojan","title":"Rethinking the Reverse-engineering of Trojan Triggers","date":"2022-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ru-system-software-and-security/featurere","path":"resnet_nole.py","file_url":"https://github.com/ru-system-software-and-security/featurere/blob/HEAD/resnet_nole.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2210.14026","paper":"/paper/swift-rapid-decentralized-federated-learning","title":"SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication","date":"2022-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umd-huang-lab/SWIFT","path":"GDM/Resnet.py","file_url":"https://github.com/umd-huang-lab/SWIFT/blob/HEAD/GDM/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2210.12873","paper":"/paper/flip-a-provable-defense-framework-for","title":"FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning","date":"2022-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaiyuanZh/FLIP","path":"models/pytorch_resnet.py","file_url":"https://github.com/KaiyuanZh/FLIP/blob/HEAD/models/pytorch_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2210.10960","paper":"/paper/diffusion-models-already-have-a-semantic","title":"Diffusion Models already have a Semantic Latent Space","date":"2022-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kwonminki/Asyrp_official","path":"losses/resnet.py","file_url":"https://github.com/kwonminki/Asyrp_official/blob/HEAD/losses/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2210.10763","paper":"/paper/on-the-feasibility-of-cross-task-transfer","title":"On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlpc-ucsd/xtra","path":"src/config/atari/model.py","file_url":"https://github.com/mlpc-ucsd/xtra/blob/HEAD/src/config/atari/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2210.10276","paper":"/paper/clip-driven-fine-grained-text-image-person-re","title":"CLIP-Driven Fine-grained Text-Image Person Re-identification","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuanglinyan/CFine","path":"models/resnet.py","file_url":"https://github.com/shuanglinyan/CFine/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2210.10276","paper":"/paper/clip-driven-fine-grained-text-image-person-re","title":"CLIP-Driven Fine-grained Text-Image Person Re-identification","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuanglinyan/CFine","path":"models/modeling_resnet.py","file_url":"https://github.com/shuanglinyan/CFine/blob/HEAD/models/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2210.10209","paper":"/paper/exclusive-supermask-subnetwork-training-for","title":"Exclusive Supermask Subnetwork Training for Continual Learning","date":"2022-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prateeky2806/exessnet","path":"models/gemresnet.py","file_url":"https://github.com/prateeky2806/exessnet/blob/HEAD/models/gemresnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4d4b05d0419192a","mcp_get_code":{"code_sha256":"d4d4b05d0419192a"}},{"arxiv_id":"2210.10090","paper":"/paper/how-to-boost-face-recognition-with-stylegan","title":"How to Boost Face Recognition with StyleGAN?","date":"2022-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seva100/stylegan-for-facerec","path":"backbone/model_resnet.py","file_url":"https://github.com/seva100/stylegan-for-facerec/blob/HEAD/backbone/model_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"999e0ea90670a179","mcp_get_code":{"code_sha256":"999e0ea90670a179"}},{"arxiv_id":"2210.08732","paper":"/paper/forecasting-human-trajectory-from-scene","title":"Forecasting Human Trajectory from Scene History","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.07242","paper":"/paper/openood-benchmarking-generalized-out-of","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"antoinedemathelin/openood","path":"openood/networks/bit.py","file_url":"https://github.com/antoinedemathelin/openood/blob/HEAD/openood/networks/bit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2210.06989","paper":"/paper/multi-task-meta-learning-learn-how-to-adapt","title":"Multi-Task Meta Learning: learn how to adapt to unseen tasks","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ricupa/mtml-learn-how-to-adapt-to-unseen-tasks","path":"src/all_model/resnet.py","file_url":"https://github.com/ricupa/mtml-learn-how-to-adapt-to-unseen-tasks/blob/HEAD/src/all_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.06780","paper":"/paper/intermediate-prototype-mining-transformer-for","title":"Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LIUYUANWEI98/IPMT","path":"model/resnet.py","file_url":"https://github.com/LIUYUANWEI98/IPMT/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2210.06704","paper":"/paper/collider-a-robust-training-framework-for","title":"COLLIDER: A Robust Training Framework for Backdoor Data","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmdolatabadi/collider","path":"models/resnet.py","file_url":"https://github.com/hmdolatabadi/collider/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.06077","paper":"/paper/double-bubble-toil-and-trouble-enhancing","title":"Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity","date":"2022-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andrew-cullen/DoubleBubble","path":"src/macer_model.py","file_url":"https://github.com/andrew-cullen/DoubleBubble/blob/HEAD/src/macer_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2210.05958","paper":"/paper/bridging-the-gap-between-vision-transformers","title":"Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets","date":"2022-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arieseirack/dhvt","path":"vision_transformer.py","file_url":"https://github.com/arieseirack/dhvt/blob/HEAD/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"429abe9dc8793925","mcp_get_code":{"code_sha256":"429abe9dc8793925"}},{"arxiv_id":"2210.05176","paper":"/paper/fine-grained-image-style-transfer-with-visual","title":"Fine-Grained Image Style Transfer with Visual Transformers","date":"2022-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/sttr","path":"models_istt/backbone.py","file_url":"https://github.com/researchmm/sttr/blob/HEAD/models_istt/backbone.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef02d64125a1bf3d","mcp_get_code":{"code_sha256":"ef02d64125a1bf3d"}},{"arxiv_id":"2210.04524","paper":"/paper/margin-based-few-shot-class-incremental","title":"Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation","date":"2022-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/clom","path":"models/resnet18_encoder.py","file_url":"https://github.com/zoilsen/clom/blob/HEAD/models/resnet18_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.04524","paper":"/paper/margin-based-few-shot-class-incremental","title":"Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation","date":"2022-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoilsen/clom","path":"models/resnet20_cifar.py","file_url":"https://github.com/zoilsen/clom/blob/HEAD/models/resnet20_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2210.04468","paper":"/paper/distill-the-image-to-nowhere-inversion","title":"Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation","date":"2022-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengr/ikd-mmt","path":"myresnet.py","file_url":"https://github.com/pengr/ikd-mmt/blob/HEAD/myresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.04206","paper":"/paper/attention-diversification-for-domain","title":"Attention Diversification for Domain Generalization","date":"2022-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/DomainGeneralization","path":"models/resnet_mixstyle.py","file_url":"https://github.com/hikvision-research/DomainGeneralization/blob/HEAD/models/resnet_mixstyle.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2210.02808","paper":"/paper/effective-self-supervised-pre-training-on-low","title":"Effective Self-supervised Pre-training on Low-compute Networks without Distillation","date":"2022-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"saic-fi/SSLight","path":"src/sslight/backbone/resnet.py","file_url":"https://github.com/saic-fi/SSLight/blob/HEAD/src/sslight/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2210.02149","paper":"/paper/relational-proxies-emergent-relationships-as","title":"Relational Proxies: Emergent Relationships as Fine-Grained Discriminators","date":"2022-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhrac/relational-proxies","path":"src/networks/resnet.py","file_url":"https://github.com/abhrac/relational-proxies/blob/HEAD/src/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2210.01917","paper":"/paper/dfferentiable-raycasting-for-self-supervised","title":"Differentiable Raycasting for Self-supervised Occupancy Forecasting","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tarashakhurana/emergent-occ-forecasting","path":"model.py","file_url":"https://github.com/tarashakhurana/emergent-occ-forecasting/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"caeaa72cca0b0383","mcp_get_code":{"code_sha256":"caeaa72cca0b0383"}},{"arxiv_id":"2210.01439","paper":"/paper/boosting-few-shot-fine-grained-recognition","title":"Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cser-tang-hao/bsfa-fsfg","path":"models/resnet12.py","file_url":"https://github.com/cser-tang-hao/bsfa-fsfg/blob/HEAD/models/resnet12.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2209.12213","paper":"/paper/eco-tr-efficient-correspondences-finding-via","title":"ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement","date":"2022-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dltan7/ECO-TR","path":"src/models/base/resnet.py","file_url":"https://github.com/dltan7/ECO-TR/blob/HEAD/src/models/base/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2209.09658","paper":"/paper/lazy-vs-hasty-linearization-in-deep-networks","title":"Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty","date":"2022-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tfjgeorge/lazy_vs_hasty","path":"exp_classif/models.py","file_url":"https://github.com/tfjgeorge/lazy_vs_hasty/blob/HEAD/exp_classif/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"15773279a9becba1","mcp_get_code":{"code_sha256":"15773279a9becba1"}},{"arxiv_id":"2209.09484","paper":"/paper/hierarchical-temporal-transformer-for-3d-hand","title":"Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos","date":"2022-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fylwen/htt","path":"models/resnet.py","file_url":"https://github.com/fylwen/htt/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2209.08183","paper":"/paper/optimal-scaling-for-locally-balanced","title":"Optimal Scaling for Locally Balanced Proposals in Discrete Spaces","date":"2022-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ha0ransun/lbp_scale","path":"lbp/model/dnn.py","file_url":"https://github.com/ha0ransun/lbp_scale/blob/HEAD/lbp/model/dnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a01b6bf3d5c907c2","mcp_get_code":{"code_sha256":"a01b6bf3d5c907c2"}},{"arxiv_id":"2209.07589","paper":"/paper/pizza-a-powerful-image-only-zero-shot-zero","title":"PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking","date":"2022-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nv-nguyen/pizza","path":"lib/model/resnet.py","file_url":"https://github.com/nv-nguyen/pizza/blob/HEAD/lib/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2209.07589","paper":"/paper/pizza-a-powerful-image-only-zero-shot-zero","title":"PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking","date":"2022-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nv-nguyen/pizza","path":"lib/model/model_utils.py","file_url":"https://github.com/nv-nguyen/pizza/blob/HEAD/lib/model/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3cf617e0b6e54333","mcp_get_code":{"code_sha256":"3cf617e0b6e54333"}},{"arxiv_id":"2209.04996","paper":"/paper/switchable-online-knowledge-distillation","title":"Switchable Online Knowledge Distillation","date":"2022-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hfutqian/SwitOKD","path":"SwitOKD_code/ImageNet/ResNet18_34/models/resnet18.py","file_url":"https://github.com/hfutqian/SwitOKD/blob/HEAD/SwitOKD_code/ImageNet/ResNet18_34/models/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2209.04996","paper":"/paper/switchable-online-knowledge-distillation","title":"Switchable Online Knowledge Distillation","date":"2022-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hfutqian/SwitOKD","path":"SwitOKD_code/CIFAR-10/WRN-16-1_8/models/wrnet16_1.py","file_url":"https://github.com/hfutqian/SwitOKD/blob/HEAD/SwitOKD_code/CIFAR-10/WRN-16-1_8/models/wrnet16_1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2209.04836","paper":"/paper/git-re-basin-merging-models-modulo","title":"Git Re-Basin: Merging Models modulo Permutation Symmetries","date":"2022-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"themrzmaster/git-re-basin-pytorch","path":"models/resnet.py","file_url":"https://github.com/themrzmaster/git-re-basin-pytorch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2209.01501","paper":"/paper/meta-learning-with-less-forgetting-on-large","title":"Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions","date":"2022-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/Semi-meta","path":"model_filter.py","file_url":"https://github.com/joey-wang123/Semi-meta/blob/HEAD/model_filter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a96935ee89a7588f","mcp_get_code":{"code_sha256":"a96935ee89a7588f"}},{"arxiv_id":"2209.00183","paper":"/paper/proco-prototype-aware-contrastive-learning","title":"ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification","date":"2022-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2208.13241","paper":"/paper/towards-accurate-reconstruction-of-3d-scene","title":"Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image","date":"2022-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aim-uofa/depth","path":"LeReS/Minist_Test/lib/Resnext_torch.py","file_url":"https://github.com/aim-uofa/depth/blob/HEAD/LeReS/Minist_Test/lib/Resnext_torch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2208.13241","paper":"/paper/towards-accurate-reconstruction-of-3d-scene","title":"Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image","date":"2022-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aim-uofa/depth","path":"LeReS/Minist_Test/lib/Resnet.py","file_url":"https://github.com/aim-uofa/depth/blob/HEAD/LeReS/Minist_Test/lib/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2208.13141","paper":"/paper/federated-learning-of-large-models-at-the","title":"Federated Learning of Large Models at the Edge via Principal Sub-Model Training","date":"2022-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuehniu/modeldecomp-fl","path":"model/resnetcifar.py","file_url":"https://github.com/yuehniu/modeldecomp-fl/blob/HEAD/model/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2208.11266","paper":"/paper/scale-online-self-supervised-lifelong","title":"SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge","date":"2022-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orienfish/scale","path":"networks/resnet_pnn.py","file_url":"https://github.com/orienfish/scale/blob/HEAD/networks/resnet_pnn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2208.11266","paper":"/paper/scale-online-self-supervised-lifelong","title":"SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge","date":"2022-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orienfish/scale","path":"networks/resnet_big.py","file_url":"https://github.com/orienfish/scale/blob/HEAD/networks/resnet_big.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2208.10992","paper":"/paper/unsupervised-anomaly-localization-with","title":"Unsupervised Anomaly Localization with Structural Feature-Autoencoders","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"felime/feature-autoencoder","path":"fae/baselines/fpi/model.py","file_url":"https://github.com/felime/feature-autoencoder/blob/HEAD/fae/baselines/fpi/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2208.10559","paper":"/paper/transductive-decoupled-variational-inference","title":"Transductive Decoupled Variational Inference for Few-Shot Classification","date":"2022-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anujinho/trident","path":"src/zoo/archs.py","file_url":"https://github.com/anujinho/trident/blob/HEAD/src/zoo/archs.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2208.10335","paper":"/paper/intensity-aware-loss-for-dynamic-facial","title":"Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild","date":"2022-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muse1998/IAL-for-Facial-Expression-Recognition","path":"modules.py","file_url":"https://github.com/muse1998/IAL-for-Facial-Expression-Recognition/blob/HEAD/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4868cd8dd08ab632","mcp_get_code":{"code_sha256":"4868cd8dd08ab632"}},{"arxiv_id":"2208.09910","paper":"/paper/revisiting-weak-to-strong-consistency-in-semi","title":"Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation","date":"2022-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiheYoung/UniMatch","path":"model/backbone/resnet.py","file_url":"https://github.com/LiheYoung/UniMatch/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2208.09833","paper":"/paper/combating-noisy-labeled-and-imbalanced-data","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","date":"2022-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2208.09754","paper":"/paper/flis-clustered-federated-learning-via","title":"FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution","date":"2022-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mmorafah/flis","path":"src/models/resnetcifar.py","file_url":"https://github.com/mmorafah/flis/blob/HEAD/src/models/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2208.08914","paper":"/paper/prompt-vision-transformer-for-domain","title":"Prompt Vision Transformer for Domain Generalization","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengzangw/DoPrompt","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/zhengzangw/DoPrompt/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2208.05924","paper":"/paper/regularizing-deep-neural-networks-with-1","title":"Regularizing Deep Neural Networks with Stochastic Estimators of Hessian Trace","date":"2022-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2208.05424","paper":"/paper/generating-physically-consistent-high","title":"Hard-Constrained Deep Learning for Climate Downscaling","date":"2022-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rolnicklab/constrained-downscaling","path":"models.py","file_url":"https://github.com/rolnicklab/constrained-downscaling/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2208.05244","paper":"/paper/learning-degradation-representations-for","title":"Learning Degradation Representations for Image Deblurring","date":"2022-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dasongli1/learning_degradation","path":"basicsr/models/archs/msdi_arch.py","file_url":"https://github.com/dasongli1/learning_degradation/blob/HEAD/basicsr/models/archs/msdi_arch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"822614ced2dcad7a","mcp_get_code":{"code_sha256":"822614ced2dcad7a"}},{"arxiv_id":"2208.04187","paper":"/paper/towards-memory-efficient-training-via-dual","title":"DIVISION: Memory Efficient Training via Dual Activation Precision","date":"2022-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guanchuwang/division","path":"cifar_models/resnet.py","file_url":"https://github.com/guanchuwang/division/blob/HEAD/cifar_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2208.00632","paper":"/paper/multi-spectral-vehicle-re-identification-with","title":"Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark","date":"2022-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"superlollipop123/cross-directional-center-network-and-msvr310","path":"modeling/baseline_zxp.py","file_url":"https://github.com/superlollipop123/cross-directional-center-network-and-msvr310/blob/HEAD/modeling/baseline_zxp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"590d3cbda981aa4f","mcp_get_code":{"code_sha256":"590d3cbda981aa4f"}},{"arxiv_id":"2208.00780","paper":"/paper/visual-correspondence-based-explanations","title":"Visual correspondence-based explanations improve AI robustness and human-AI team accuracy","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anguyen8/visual-correspondence-xai","path":"EMD-Corr/src/cub200_features.py","file_url":"https://github.com/anguyen8/visual-correspondence-xai/blob/HEAD/EMD-Corr/src/cub200_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2208.00275","paper":"/paper/revisiting-the-critical-factors-of","title":"Revisiting the Critical Factors of Augmentation-Invariant Representation Learning","date":"2022-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/revisitairl","path":"models/resnet.py","file_url":"https://github.com/megvii-research/revisitairl/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2207.14476","paper":"/paper/centrality-and-consistency-two-stage-clean","title":"Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels","date":"2022-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uitrbn/TSCSI_IDN","path":"PreResNet.py","file_url":"https://github.com/uitrbn/TSCSI_IDN/blob/HEAD/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2207.13916","paper":"/paper/a-novel-data-augmentation-technique-for-out","title":"A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions","date":"2022-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cnc-ood/cnc_ood","path":"models/resnet_tinyimagenet.py","file_url":"https://github.com/cnc-ood/cnc_ood/blob/HEAD/models/resnet_tinyimagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ace1c98fa5f2cd5","mcp_get_code":{"code_sha256":"2ace1c98fa5f2cd5"}},{"arxiv_id":"2207.13686","paper":"/paper/shift-tolerant-perceptual-similarity-metric-1","title":"Shift-tolerant Perceptual Similarity Metric","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhijay9/shifttolerant-lpips","path":"models_lpf/resnet.py","file_url":"https://github.com/abhijay9/shifttolerant-lpips/blob/HEAD/models_lpf/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"2207.13686","paper":"/paper/shift-tolerant-perceptual-similarity-metric-1","title":"Shift-tolerant Perceptual Similarity Metric","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhijay9/shifttolerant-lpips","path":"models_lpf/vgg_w_skip.py","file_url":"https://github.com/abhijay9/shifttolerant-lpips/blob/HEAD/models_lpf/vgg_w_skip.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"67c9ab8e625b4e5f","mcp_get_code":{"code_sha256":"67c9ab8e625b4e5f"}},{"arxiv_id":"2207.13374","paper":"/paper/efficient-video-deblurring-guided-by-motion","title":"Efficient Video Deblurring Guided by Motion Magnitude","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2207.13362","paper":"/paper/camouflaged-object-detection-via-context","title":"Camouflaged Object Detection via Context-aware Cross-level Fusion","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thograce/C2FNet","path":"lib/ResNet.py","file_url":"https://github.com/thograce/C2FNet/blob/HEAD/lib/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.13048","paper":"/paper/domain-adaptation-under-open-set-label-shift","title":"Domain Adaptation under Open Set Label Shift","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/DANCE","path":"models/resnet.py","file_url":"https://github.com/VisionLearningGroup/DANCE/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.12394","paper":"/paper/dynamic-3d-scene-analysis-by-point-cloud","title":"Dynamic 3D Scene Analysis by Point Cloud Accumulation","date":"2022-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prs-eth/PCAccumulation","path":"models/unet.py","file_url":"https://github.com/prs-eth/PCAccumulation/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe1bac001ffc64d5","mcp_get_code":{"code_sha256":"fe1bac001ffc64d5"}},{"arxiv_id":"2207.11718","paper":"/paper/tips-text-induced-pose-synthesis","title":"TIPS: Text-Induced Pose Synthesis","date":"2022-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prasunroy/tips","path":"demo/tips/models/pose2pose.py","file_url":"https://github.com/prasunroy/tips/blob/HEAD/demo/tips/models/pose2pose.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"765da6946c79c4aa","mcp_get_code":{"code_sha256":"765da6946c79c4aa"}},{"arxiv_id":"2207.10792","paper":"/paper/test-time-adaptation-via-self-training-with","title":"Test-Time Adaptation via Self-Training with Nearest Neighbor Information","date":"2022-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingukjang/tast","path":"domainbed/lib/big_transfer.py","file_url":"https://github.com/mingukjang/tast/blob/HEAD/domainbed/lib/big_transfer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2207.09697","paper":"/paper/robust-object-detection-with-inaccurate","title":"Robust Object Detection With Inaccurate Bounding Boxes","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cxliu0/OA-MIL","path":"mmcv/mmcv/cnn/resnet.py","file_url":"https://github.com/cxliu0/OA-MIL/blob/HEAD/mmcv/mmcv/cnn/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b257c85a5945d5eb","mcp_get_code":{"code_sha256":"b257c85a5945d5eb"}},{"arxiv_id":"2207.09291","paper":"/paper/3d-room-layout-estimation-from-a-cubemap-of","title":"3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform","date":"2022-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Starrah/DMH-Net","path":"model.py","file_url":"https://github.com/Starrah/DMH-Net/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"2207.09176","paper":"/paper/self-supervision-can-be-a-good-few-shot","title":"Self-Supervision Can Be a Good Few-Shot Learner","date":"2022-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbbdylan/unisiam","path":"model/resnet.py","file_url":"https://github.com/bbbdylan/unisiam/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2207.08531","paper":"/paper/did-m3d-decoupling-instance-depth-for","title":"DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spengliang/did-m3d","path":"lib/backbones/dla.py","file_url":"https://github.com/spengliang/did-m3d/blob/HEAD/lib/backbones/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.08531","paper":"/paper/did-m3d-decoupling-instance-depth-for","title":"DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spengliang/did-m3d","path":"lib/backbones/resnet.py","file_url":"https://github.com/spengliang/did-m3d/blob/HEAD/lib/backbones/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c875390a9fab8007","mcp_get_code":{"code_sha256":"c875390a9fab8007"}},{"arxiv_id":"2207.07840","paper":"/paper/class-incremental-lifelong-learning-in-multi","title":"Class-Incremental Lifelong Learning in Multi-Label Classification","date":"2022-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaile-du/agcn","path":"AGCN-LML/myresnet_fc.py","file_url":"https://github.com/kaile-du/agcn/blob/HEAD/AGCN-LML/myresnet_fc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.07517","paper":"/paper/on-the-usefulness-of-deep-ensemble-diversity","title":"On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection","date":"2022-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guoxoug/ens-div-ood-detect","path":"models/resnet.py","file_url":"https://github.com/guoxoug/ens-div-ood-detect/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2207.07506","paper":"/paper/augmenting-softmax-information-for-selective","title":"Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data","date":"2022-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guoxoug/sirc","path":"models/resnet.py","file_url":"https://github.com/guoxoug/sirc/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2207.07506","paper":"/paper/augmenting-softmax-information-for-selective","title":"Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data","date":"2022-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guoxoug/sirc","path":"models/resnet_v2.py","file_url":"https://github.com/guoxoug/sirc/blob/HEAD/models/resnet_v2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2207.06267","paper":"/paper/task-agnostic-representation-consolidation-a","title":"Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach","date":"2022-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/tarc","path":"backbone/ResNet18.py","file_url":"https://github.com/neurai-lab/tarc/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2207.05801","paper":"/paper/relaxloss-defending-membership-inference-1","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DingfanChen/RelaxLoss","path":"source/cifar/models/preresnet.py","file_url":"https://github.com/DingfanChen/RelaxLoss/blob/HEAD/source/cifar/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.05801","paper":"/paper/relaxloss-defending-membership-inference-1","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DingfanChen/RelaxLoss","path":"source/cifar/models/resnet.py","file_url":"https://github.com/DingfanChen/RelaxLoss/blob/HEAD/source/cifar/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dec1b673b8a327b","mcp_get_code":{"code_sha256":"4dec1b673b8a327b"}},{"arxiv_id":"2207.05306","paper":"/paper/contrastive-deep-supervision","title":"Contrastive Deep Supervision","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"archiplab-linfengzhang/contrastive-deep-supervision","path":"CIFAR/resnet.py","file_url":"https://github.com/archiplab-linfengzhang/contrastive-deep-supervision/blob/HEAD/CIFAR/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2207.05306","paper":"/paper/contrastive-deep-supervision","title":"Contrastive Deep Supervision","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"archiplab-linfengzhang/contrastive-deep-supervision","path":"ImageNet/Contrastive_Deep_Supervision/resnet.py","file_url":"https://github.com/archiplab-linfengzhang/contrastive-deep-supervision/blob/HEAD/ImageNet/Contrastive_Deep_Supervision/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2207.04998","paper":"/paper/consistency-is-the-key-to-further-mitigating","title":"Consistency is the key to further mitigating catastrophic forgetting in continual learning","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/consistencycl","path":"backbone/ResNet18.py","file_url":"https://github.com/neurai-lab/consistencycl/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2207.01145","paper":"/paper/it-s-all-about-consistency-a-study-on-memory","title":"Memory Population in Continual Learning via Outlier Elimination","date":"2022-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JuliousHurtado/MOE","path":"models/resnet.py","file_url":"https://github.com/JuliousHurtado/MOE/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2206.15398","paper":"/paper/polarformer-multi-camera-3d-object-detection","title":"PolarFormer: Multi-camera 3D Object Detection with Polar Transformer","date":"2022-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/polarformer","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/fudan-zvg/polarformer/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2206.10550","paper":"/paper/certified-adversarial-robustness-for-free","title":"(Certified!!) Adversarial Robustness for Free!","date":"2022-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blaisedelattre/bridging_the_gap_rs","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/blaisedelattre/bridging_the_gap_rs/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2206.08898","paper":"/paper/sima-simple-softmax-free-attention-for-vision","title":"SimA: Simple Softmax-free Attention for Vision Transformers","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucdvision/sima","path":"sima.py","file_url":"https://github.com/ucdvision/sima/blob/HEAD/sima.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"429abe9dc8793925","mcp_get_code":{"code_sha256":"429abe9dc8793925"}},{"arxiv_id":"2206.08671","paper":"/paper/fit-parameter-efficient-few-shot-transfer","title":"FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/fit","path":"src/bit_resnet.py","file_url":"https://github.com/cambridge-mlg/fit/blob/HEAD/src/bit_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2206.07290","paper":"/paper/differentiable-top-k-classification-learning-1","title":"Differentiable Top-k Classification Learning","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"felix-petersen/difftopk","path":"experiments/utils/resnet_cifar10.py","file_url":"https://github.com/felix-petersen/difftopk/blob/HEAD/experiments/utils/resnet_cifar10.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2206.07125","paper":"/paper/self-supervised-pretraining-for","title":"Self-Supervised Pretraining for Differentially Private Learning","date":"2022-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UnchartedRLab/SSP","path":"scatsim/extract_total.py","file_url":"https://github.com/UnchartedRLab/SSP/blob/HEAD/scatsim/extract_total.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2206.06293","paper":"/paper/learning-domain-adaptive-object-detection","title":"Learning Domain Adaptive Object Detection with Probabilistic Teacher","date":"2022-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/probabilisticteacher","path":"pt/modeling/utils.py","file_url":"https://github.com/hikvision-research/probabilisticteacher/blob/HEAD/pt/modeling/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2206.04016","paper":"/paper/synergy-between-synaptic-consolidation-and","title":"SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning","date":"2022-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/synergy","path":"backbone/ResNet18.py","file_url":"https://github.com/neurai-lab/synergy/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2206.03428","paper":"/paper/revealing-single-frame-bias-for-video-and","title":"Revealing Single Frame Bias for Video-and-Language Learning","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jayleicn/ClipBERT","path":"src/modeling/grid_feat.py","file_url":"https://github.com/jayleicn/ClipBERT/blob/HEAD/src/modeling/grid_feat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17e89eef72e37efe","mcp_get_code":{"code_sha256":"17e89eef72e37efe"}},{"arxiv_id":"2206.02780","paper":"/paper/gensdf-two-stage-learning-of-generalizable","title":"GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions","date":"2022-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-computational-imaging/gensdf","path":"model/archs/unet.py","file_url":"https://github.com/princeton-computational-imaging/gensdf/blob/HEAD/model/archs/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe1bac001ffc64d5","mcp_get_code":{"code_sha256":"fe1bac001ffc64d5"}},{"arxiv_id":"2206.02659","paper":"/paper/robust-fine-tuning-of-deep-neural-networks","title":"Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees","date":"2022-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VirtuosoResearch/Robust-fine-tuning","path":"exps_on_image_datasets/model/modeling_resnet.py","file_url":"https://github.com/VirtuosoResearch/Robust-fine-tuning/blob/HEAD/exps_on_image_datasets/model/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2206.02066","paper":"/paper/pidnet-a-real-time-semantic-segmentation","title":"PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers","date":"2022-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Darth-Kronos/PIDNet_TensorRT","path":"models/others/bisenet_adb_bag.py","file_url":"https://github.com/Darth-Kronos/PIDNet_TensorRT/blob/HEAD/models/others/bisenet_adb_bag.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2206.02066","paper":"/paper/pidnet-a-real-time-semantic-segmentation","title":"PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers","date":"2022-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hamidriasat/PIDNet","path":"resnet.py","file_url":"https://github.com/hamidriasat/PIDNet/blob/HEAD/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa1dd696ceb3bffe","mcp_get_code":{"code_sha256":"fa1dd696ceb3bffe"}},{"arxiv_id":"2206.01934","paper":"/paper/stochastic-multiple-target-sampling-gradient","title":"Stochastic Multiple Target Sampling Gradient Descent","date":"2022-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isl-org/MultiObjectiveOptimization","path":"multi_task/models/pspnet.py","file_url":"https://github.com/isl-org/MultiObjectiveOptimization/blob/HEAD/multi_task/models/pspnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"2206.01934","paper":"/paper/stochastic-multiple-target-sampling-gradient","title":"Stochastic Multiple Target Sampling Gradient Descent","date":"2022-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isl-org/MultiObjectiveOptimization","path":"multi_task/models/resnet_mit.py","file_url":"https://github.com/isl-org/MultiObjectiveOptimization/blob/HEAD/multi_task/models/resnet_mit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90e50bc6f1220bdd","mcp_get_code":{"code_sha256":"90e50bc6f1220bdd"}},{"arxiv_id":"2205.13720","paper":"/paper/effective-abstract-reasoning-with-dual-1","title":"Effective Abstract Reasoning with Dual-Contrast Network","date":"2022-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visiontao/dcnet","path":"dcnet.py","file_url":"https://github.com/visiontao/dcnet/blob/HEAD/dcnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"52601677ce5d1634","mcp_get_code":{"code_sha256":"52601677ce5d1634"}},{"arxiv_id":"2205.13720","paper":"/paper/effective-abstract-reasoning-with-dual-1","title":"Effective Abstract Reasoning with Dual-Contrast Network","date":"2022-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visiontao/dcnet","path":"dcnet.py","file_url":"https://github.com/visiontao/dcnet/blob/HEAD/dcnet.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e065efb89e99d425","mcp_get_code":{"code_sha256":"e065efb89e99d425"}},{"arxiv_id":"2205.13462","paper":"/paper/fedaug-reducing-the-local-learning-bias","title":"FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lins-lab/fedbr","path":"fedbr/Resnet.py","file_url":"https://github.com/lins-lab/fedbr/blob/HEAD/fedbr/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2aa5d536ff654fac","mcp_get_code":{"code_sha256":"2aa5d536ff654fac"}},{"arxiv_id":"2205.13452","paper":"/paper/continual-evaluation-for-lifelong-learning","title":"Continual evaluation for lifelong learning: Identifying the stability gap","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mattdl/ContinualEvaluation","path":"src/model.py","file_url":"https://github.com/mattdl/ContinualEvaluation/blob/HEAD/src/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2205.13452","paper":"/paper/continual-evaluation-for-lifelong-learning","title":"Continual evaluation for lifelong learning: Identifying the stability gap","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mattdl/ContinualEvaluation","path":"avalanche/models/icarl_resnet.py","file_url":"https://github.com/mattdl/ContinualEvaluation/blob/HEAD/avalanche/models/icarl_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f522c857bf857a12","mcp_get_code":{"code_sha256":"f522c857bf857a12"}},{"arxiv_id":"2205.13282","paper":"/paper/on-the-eigenvalues-of-global-covariance","title":"On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KingJamesSong/DifferentiableSVD","path":"src/network/resnet.py","file_url":"https://github.com/KingJamesSong/DifferentiableSVD/blob/HEAD/src/network/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2205.12693","paper":"/paper/contrastive-learning-with-boosted","title":"Contrastive Learning with Boosted Memorization","date":"2022-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/BCL","path":"models/resnet.py","file_url":"https://github.com/MediaBrain-SJTU/BCL/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2205.12141","paper":"/paper/one-pixel-shortcut-on-the-learning-preference","title":"One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks","date":"2022-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cychomatica/one-pixel-shotcut","path":"model/WideResNet.py","file_url":"https://github.com/cychomatica/one-pixel-shotcut/blob/HEAD/model/WideResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2205.09739","paper":"/paper/diverse-weight-averaging-for-out-of","title":"Diverse Weight Averaging for Out-of-Distribution Generalization","date":"2022-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexrame/diwa","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/alexrame/diwa/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2205.09723","paper":"/paper/robust-and-efficient-medical-imaging-with","title":"Robust and Efficient Medical Imaging with Self-Supervision","date":"2022-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/big_transfer","path":"bit_pytorch/models.py","file_url":"https://github.com/google-research/big_transfer/blob/HEAD/bit_pytorch/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2205.07246","paper":"/paper/freematch-self-adaptive-thresholding-for-semi","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","date":"2022-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siyi-wind/STiL","path":"models/resnets.py","file_url":"https://github.com/siyi-wind/STiL/blob/HEAD/models/resnets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c5e915e62a479bd7","mcp_get_code":{"code_sha256":"c5e915e62a479bd7"}},{"arxiv_id":"2205.07179","paper":"/paper/promoting-saliency-from-depth-deep-1","title":"Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection","date":"2022-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiwei0921/dsu","path":"DSU_Code/model/ResNet.py","file_url":"https://github.com/jiwei0921/dsu/blob/HEAD/DSU_Code/model/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2205.06701","paper":"/paper/knowledge-distillation-meets-open-set-semi","title":"Knowledge Distillation Meets Open-Set Semi-Supervised Learning","date":"2022-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingyang2017/srd_ossl","path":"models/resnet.py","file_url":"https://github.com/jingyang2017/srd_ossl/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2204.13779","paper":"/paper/formulating-robustness-against-unforeseen","title":"Formulating Robustness Against Unforeseen Attacks","date":"2022-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inspire-group/variation-regularization","path":"train/ResNet.py","file_url":"https://github.com/inspire-group/variation-regularization/blob/HEAD/train/ResNet.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9ec14cd0fc7f9260","mcp_get_code":{"code_sha256":"9ec14cd0fc7f9260"}},{"arxiv_id":"2204.13091","paper":"/paper/attention-consistency-on-visual-corruptions","title":"Attention Consistency on Visual Corruptions for Single-Source Domain Generalization","date":"2022-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainableml/acvc","path":"models/ResNet.py","file_url":"https://github.com/explainableml/acvc/blob/HEAD/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2204.11448","paper":"/paper/high-efficiency-lossy-image-coding-through","title":"High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation","date":"2022-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lumingzzz/tinylic","path":"compressai/layers/layers.py","file_url":"https://github.com/lumingzzz/tinylic/blob/HEAD/compressai/layers/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0cbeed6985b2cf30","mcp_get_code":{"code_sha256":"0cbeed6985b2cf30"}},{"arxiv_id":"2204.10839","paper":"/paper/how-sampling-impacts-the-robustness-of","title":"How Sampling Impacts the Robustness of Stochastic Neural Networks","date":"2022-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"meliketoy/wide-resnet.pytorch","path":"networks/wide_resnet.py","file_url":"https://github.com/meliketoy/wide-resnet.pytorch/blob/HEAD/networks/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2204.10144","paper":"/paper/a-case-for-using-rotation-invariant-features","title":"A case for using rotation invariant features in state of the art feature matchers","date":"2022-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"georg-bn/se2-loftr","path":"src/loftr/backbone/resnet_fpn.py","file_url":"https://github.com/georg-bn/se2-loftr/blob/HEAD/src/loftr/backbone/resnet_fpn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2204.08491","paper":"/paper/active-learning-helps-pretrained-models-learn","title":"Active Learning Helps Pretrained Models Learn the Intended Task","date":"2022-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alextamkin/active-learning-pretrained-models","path":"models/architecture.py","file_url":"https://github.com/alextamkin/active-learning-pretrained-models/blob/HEAD/models/architecture.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2204.07305","paper":"/paper/pushing-the-limits-of-simple-pipelines-for","title":"Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference","date":"2022-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hushell/pmf_cvpr22","path":"models/resnet_v2.py","file_url":"https://github.com/hushell/pmf_cvpr22/blob/HEAD/models/resnet_v2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2204.07249","paper":"/paper/minimizing-control-for-credit-assignment-with","title":"Minimizing Control for Credit Assignment with Strong Feedback","date":"2022-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pennfranc/bio-inspired-continual-learning","path":"Continual-Learning-Benchmark/models/resnet.py","file_url":"https://github.com/pennfranc/bio-inspired-continual-learning/blob/HEAD/Continual-Learning-Benchmark/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2204.05041","paper":"/paper/pyramid-grafting-network-for-one-stage-high","title":"Pyramid Grafting Network for One-Stage High Resolution Saliency Detection","date":"2022-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icvteam/pgnet","path":"src/Res.py","file_url":"https://github.com/icvteam/pgnet/blob/HEAD/src/Res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2204.04676","paper":"/paper/simple-baselines-for-image-restoration","title":"Simple Baselines for Image Restoration","date":"2022-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"murufeng/FUIR","path":"basicsr/models/archs/HINet_arch.py","file_url":"https://github.com/murufeng/FUIR/blob/HEAD/basicsr/models/archs/HINet_arch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"822614ced2dcad7a","mcp_get_code":{"code_sha256":"822614ced2dcad7a"}},{"arxiv_id":"2204.02977","paper":"/paper/multi-scale-memory-based-video-deblurring","title":"Multi-Scale Memory-Based Video Deblurring","date":"2022-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jibo27/MemDeblur","path":"model/modules/mod_resnet.py","file_url":"https://github.com/jibo27/MemDeblur/blob/HEAD/model/modules/mod_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6540826b74b9e7b","mcp_get_code":{"code_sha256":"e6540826b74b9e7b"}},{"arxiv_id":"2204.02445","paper":"/paper/chore-contact-human-and-object-reconstruction","title":"CHORE: Contact, Human and Object REconstruction from a single RGB image","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiexh20/CHORE","path":"model/chore.py","file_url":"https://github.com/xiexh20/CHORE/blob/HEAD/model/chore.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"2204.02426","paper":"/paper/occamnets-mitigating-dataset-bias-by-favoring","title":"OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"erobic/occam-nets-v1","path":"models/variable_width_resnet.py","file_url":"https://github.com/erobic/occam-nets-v1/blob/HEAD/models/variable_width_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.16787","paper":"/paper/reflection-and-rotation-symmetry-detection","title":"Reflection and Rotation Symmetry Detection via Equivariant Learning","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ahyunSeo/EquiSym","path":"modeling/resnet.py","file_url":"https://github.com/ahyunSeo/EquiSym/blob/HEAD/modeling/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2203.16172","paper":"/paper/self-distillation-from-the-last-mini-batch","title":"Self-Distillation from the Last Mini-Batch for Consistency Regularization","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Meta-knowledge-Lab/DLB","path":"models/resnet.py","file_url":"https://github.com/Meta-knowledge-Lab/DLB/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.15965","paper":"/paper/psmnet-position-aware-stereo-merging-network","title":"PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2203.15516","paper":"/paper/rich-feature-construction-for-the","title":"Rich Feature Construction for the Optimization-Generalization Dilemma","date":"2022-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.15375","paper":"/paper/a-style-aware-discriminator-for-controllable","title":"A Style-aware Discriminator for Controllable Image Translation","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kunheek/style-aware-discriminator","path":"model/networks/discriminator.py","file_url":"https://github.com/kunheek/style-aware-discriminator/blob/HEAD/model/networks/discriminator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f3104b349f5f6260","mcp_get_code":{"code_sha256":"f3104b349f5f6260"}},{"arxiv_id":"2203.14542","paper":"/paper/unicon-combating-label-noise-through-uniform","title":"UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nazmul-karim170/unicon-noisy-label","path":"PreResNet_clothing1M.py","file_url":"https://github.com/nazmul-karim170/unicon-noisy-label/blob/HEAD/PreResNet_clothing1M.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.14542","paper":"/paper/unicon-combating-label-noise-through-uniform","title":"UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nazmul-karim170/unicon-noisy-label","path":"PreResNet_cifar.py","file_url":"https://github.com/nazmul-karim170/unicon-noisy-label/blob/HEAD/PreResNet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2203.14145","paper":"/paper/reverse-engineering-of-imperceptible-1","title":"Reverse Engineering of Imperceptible Adversarial Image Perturbations","date":"2022-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations","path":"archs/cifar_resnet.py","file_url":"https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations/blob/HEAD/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.13834","paper":"/paper/a-stitch-in-time-saves-nine-a-train-time","title":"A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdca-loss/mdca-calibration","path":"models/resnet.py","file_url":"https://github.com/mdca-loss/mdca-calibration/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.13834","paper":"/paper/a-stitch-in-time-saves-nine-a-train-time","title":"A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdca-loss/mdca-calibration","path":"models/resnet_imagenet.py","file_url":"https://github.com/mdca-loss/mdca-calibration/blob/HEAD/models/resnet_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ace1c98fa5f2cd5","mcp_get_code":{"code_sha256":"2ace1c98fa5f2cd5"}},{"arxiv_id":"2203.13815","paper":"/paper/versatile-multi-modal-pre-training-for-human","title":"Versatile Multi-Modal Pre-Training for Human-Centric Perception","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongfz16/hcmoco","path":"A2J/hrnet/official_hrnet.py","file_url":"https://github.com/hongfz16/hcmoco/blob/HEAD/A2J/hrnet/official_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.13815","paper":"/paper/versatile-multi-modal-pre-training-for-human","title":"Versatile Multi-Modal Pre-Training for Human-Centric Perception","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongfz16/hcmoco","path":"A2J/resnet.py","file_url":"https://github.com/hongfz16/hcmoco/blob/HEAD/A2J/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25d0e9bb83f15fe8","mcp_get_code":{"code_sha256":"25d0e9bb83f15fe8"}},{"arxiv_id":"2203.13556","paper":"/paper/deformable-butterfly-a-highly-structured-and-1","title":"Deformable Butterfly: A Highly Structured and Sparse Linear Transform","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruilin0212/debut","path":"Fine_tuning/models/resnet.py","file_url":"https://github.com/ruilin0212/debut/blob/HEAD/Fine_tuning/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2203.12817","paper":"/paper/continual-learning-and-private-unlearning","title":"Continual Learning and Private Unlearning","date":"2022-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cranial-xix/continual-learning-private-unlearning","path":"model/backbone.py","file_url":"https://github.com/cranial-xix/continual-learning-private-unlearning/blob/HEAD/model/backbone.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2203.12614","paper":"/paper/unsupervised-salient-object-detection-with","title":"Unsupervised Salient Object Detection with Spectral Cluster Voting","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noelshin/selfmask","path":"networks/resnet_models.py","file_url":"https://github.com/noelshin/selfmask/blob/HEAD/networks/resnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.11593","paper":"/paper/unified-negative-pair-generation-toward-well","title":"Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition","date":"2022-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jung-jun-uk/unpg","path":"recognition/models/iresnet.py","file_url":"https://github.com/jung-jun-uk/unpg/blob/HEAD/recognition/models/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2203.10981","paper":"/paper/monodtr-monocular-3d-object-detection-with","title":"MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kuanchihhuang/monodtr","path":"visualDet3D/networks/backbones/dla.py","file_url":"https://github.com/kuanchihhuang/monodtr/blob/HEAD/visualDet3D/networks/backbones/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.10789","paper":"/paper/domain-generalization-by-mutual-information","title":"Domain Generalization by Mutual-Information Regularization with Pre-trained Models","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/miro","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/kakaobrain/miro/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2203.10642","paper":"/paper/futr3d-a-unified-sensor-fusion-framework-for","title":"FUTR3D: A Unified Sensor Fusion Framework for 3D Detection","date":"2022-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-mars-lab/futr3d","path":"plugin/futr3d/models/backbone/vovnet.py","file_url":"https://github.com/tsinghua-mars-lab/futr3d/blob/HEAD/plugin/futr3d/models/backbone/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2203.10543","paper":"/paper/document-dewarping-with-control-points","title":"Document Dewarping with Control Points","date":"2022-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gwxie/document-dewarping-with-control-points","path":"Source/network.py","file_url":"https://github.com/gwxie/document-dewarping-with-control-points/blob/HEAD/Source/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74c75e8b88501d79","mcp_get_code":{"code_sha256":"74c75e8b88501d79"}},{"arxiv_id":"2203.09645","paper":"/paper/matchformer-interleaving-attention-in","title":"MatchFormer: Interleaving Attention in Transformers for Feature Matching","date":"2022-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamycheung/matchformer","path":"model/backbone/match_LA_large.py","file_url":"https://github.com/jamycheung/matchformer/blob/HEAD/model/backbone/match_LA_large.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2203.08450","paper":"/paper/the-devil-is-in-the-details-window-based","title":"The Devil Is in the Details: Window-based Attention for Image Compression","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"googolxx/stf","path":"compressai/layers/layers.py","file_url":"https://github.com/googolxx/stf/blob/HEAD/compressai/layers/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0cbeed6985b2cf30","mcp_get_code":{"code_sha256":"0cbeed6985b2cf30"}},{"arxiv_id":"2203.08392","paper":"/paper/patch-fool-are-vision-transformers-always-1","title":"Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RICE-EIC/Patch-Fool","path":"models/resnet.py","file_url":"https://github.com/RICE-EIC/Patch-Fool/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2203.08392","paper":"/paper/patch-fool-are-vision-transformers-always-1","title":"Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RICE-EIC/Patch-Fool","path":"models/modeling_resnet.py","file_url":"https://github.com/RICE-EIC/Patch-Fool/blob/HEAD/models/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2203.08243","paper":"/paper/unified-visual-transformer-compression-1","title":"Unified Visual Transformer Compression","date":"2022-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2203.07615","paper":"/paper/learning-what-not-to-segment-a-new","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","date":"2022-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chunbolang/BAM","path":"model/resnet.py","file_url":"https://github.com/chunbolang/BAM/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.07003","paper":"/paper/mtldesc-looking-wider-to-describe-better","title":"MTLDesc: Looking Wider to Describe Better","date":"2022-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vignywang/MTLDesc","path":"nets/vit/vit_seg_modeling_resnet_skip.py","file_url":"https://github.com/vignywang/MTLDesc/blob/HEAD/nets/vit/vit_seg_modeling_resnet_skip.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2203.05180","paper":"/paper/knowledge-distillation-as-efficient-pre","title":"Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability","date":"2022-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CVMI-Lab/KDEP","path":"src/resnet.py","file_url":"https://github.com/CVMI-Lab/KDEP/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2203.04967","paper":"/paper/unext-mlp-based-rapid-medical-image","title":"UNeXt: MLP-based Rapid Medical Image Segmentation Network","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeya-maria-jose/Medical-Transformer","path":"lib/models/resnet.py","file_url":"https://github.com/jeya-maria-jose/Medical-Transformer/blob/HEAD/lib/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.04967","paper":"/paper/unext-mlp-based-rapid-medical-image","title":"UNeXt: MLP-based Rapid Medical Image Segmentation Network","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeya-maria-jose/Medical-Transformer","path":"extractors.py","file_url":"https://github.com/jeya-maria-jose/Medical-Transformer/blob/HEAD/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"2203.04571","paper":"/paper/a-neuro-vector-symbolic-architecture-for","title":"A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/neuro-vector-symbolic-architectures","path":"nvsa/perception/resnet.py","file_url":"https://github.com/ibm/neuro-vector-symbolic-architectures/blob/HEAD/nvsa/perception/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2203.04559","paper":"/paper/learning-temporal-consistency-for-source-free","title":"Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuyu0010/ATCoN","path":"network/util.py","file_url":"https://github.com/xuyu0010/ATCoN/blob/HEAD/network/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77f89e05c55c985d","mcp_get_code":{"code_sha256":"77f89e05c55c985d"}},{"arxiv_id":"2203.03339","paper":"/paper/l2cs-net-fine-grained-gaze-estimation-in","title":"L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments","date":"2022-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7f22d053fb2c79d3","mcp_get_code":{"code_sha256":"7f22d053fb2c79d3"}},{"arxiv_id":"2202.14026","paper":"/paper/robust-training-under-label-noise-by-over","title":"Robust Training under Label Noise by Over-parameterization","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shengliu66/sop","path":"model/PreResNet.py","file_url":"https://github.com/shengliu66/sop/blob/HEAD/model/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2202.13711","paper":"/paper/evaluating-the-adversarial-robustness-of","title":"Evaluating the Adversarial Robustness of Adaptive Test-time Defenses","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/evaluating-adaptive-test-time-defenses","path":"shi_2020/resnet.py","file_url":"https://github.com/fra31/evaluating-adaptive-test-time-defenses/blob/HEAD/shi_2020/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2202.12555","paper":"/paper/6d-rotation-representation-for-unconstrained","title":"6D Rotation Representation For Unconstrained Head Pose Estimation","date":"2022-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7f22d053fb2c79d3","mcp_get_code":{"code_sha256":"7f22d053fb2c79d3"}},{"arxiv_id":"2202.10108","paper":"/paper/vitaev2-vision-transformer-advanced-by","title":"ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond","date":"2022-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tamer-saleh/s1gflood-detection","path":"resnet.py","file_url":"https://github.com/tamer-saleh/s1gflood-detection/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2202.09844","paper":"/paper/sparsity-winning-twice-better-robust-1","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","date":"2022-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-group/sparsity-win-robust-generalization","path":"Robust-Bird/models/resnet.py","file_url":"https://github.com/vita-group/sparsity-win-robust-generalization/blob/HEAD/Robust-Bird/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2202.08132","paper":"/paper/prospect-pruning-finding-trainable-weights-at-1","title":"Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients","date":"2022-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mil-ad/prospr","path":"models/resnet_custom.py","file_url":"https://github.com/mil-ad/prospr/blob/HEAD/models/resnet_custom.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2202.06592","paper":"/paper/memory-replay-with-data-compression-for-1","title":"Memory Replay with Data Compression for Continual Learning","date":"2022-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lywang3081/MRDC","path":"2_LUCIR_+DC/modified_resnet.py","file_url":"https://github.com/lywang3081/MRDC/blob/HEAD/2_LUCIR_%2BDC/modified_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2202.06187","paper":"/paper/on-the-convergence-of-clustered-federated","title":"On the Convergence of Clustered Federated Learning","date":"2022-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jie-ma-ai/FedBase","path":"fedbase/model/resnet.py","file_url":"https://github.com/jie-ma-ai/FedBase/blob/HEAD/fedbase/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2202.03958","paper":"/paper/uncertainty-modeling-for-out-of-distribution-1","title":"Uncertainty Modeling for Out-of-Distribution Generalization","date":"2022-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lixiaotong97/dsu","path":"dsu.py","file_url":"https://github.com/lixiaotong97/dsu/blob/HEAD/dsu.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"db16069021194d10","mcp_get_code":{"code_sha256":"db16069021194d10"}},{"arxiv_id":"2202.02796","paper":"/paper/glpanodepth-global-to-local-panoramic-depth","title":"GLPanoDepth: Global-to-Local Panoramic Depth Estimation","date":"2022-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeoDarcy/GLPanoDepth","path":"models/TwoBranch.py","file_url":"https://github.com/LeoDarcy/GLPanoDepth/blob/HEAD/models/TwoBranch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2647adec2b8e3196","mcp_get_code":{"code_sha256":"2647adec2b8e3196"}},{"arxiv_id":"2202.01339","paper":"/paper/understanding-cross-domain-few-shot-learning","title":"Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty","date":"2022-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungnyun/cd-fsl","path":"backbone.py","file_url":"https://github.com/sungnyun/cd-fsl/blob/HEAD/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2201.11528","paper":"/paper/beyond-imagenet-attack-towards-crafting-1","title":"Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains","date":"2022-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alibaba-AAIG/Beyond-ImageNet-Attack","path":"imagenet/resnet.py","file_url":"https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/HEAD/imagenet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2201.10703","paper":"/paper/anomaly-detection-via-reverse-distillation","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","date":"2022-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2201.09701","paper":"/paper/learning-semantics-for-visual-place","title":"Learning Semantics for Visual Place Recognition through Multi-Scale Attention","date":"2022-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeriopaolicelli/SegVPR","path":"src/net/resnet_vpr.py","file_url":"https://github.com/valeriopaolicelli/SegVPR/blob/HEAD/src/net/resnet_vpr.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2201.08361","paper":"/paper/stitch-it-in-time-gan-based-facial-editing-of","title":"Stitch it in Time: GAN-Based Facial Editing of Real Videos","date":"2022-01-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rotemtzaban/STIT","path":"models/seg_model_2.py","file_url":"https://github.com/rotemtzaban/STIT/blob/HEAD/models/seg_model_2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2201.07703","paper":"/paper/q-vit-fully-differentiable-quantization-for","title":"Q-ViT: Fully Differentiable Quantization for Vision Transformer","date":"2022-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhexinli/Q-ViT-DeiT","path":"quantization/binary_layer.py","file_url":"https://github.com/zhexinli/Q-ViT-DeiT/blob/HEAD/quantization/binary_layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2201.05131","paper":"/paper/simreg-regression-as-a-simple-yet-effective","title":"SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation","date":"2022-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucdvision/simreg","path":"models/resnet.py","file_url":"https://github.com/ucdvision/simreg/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2201.05131","paper":"/paper/simreg-regression-as-a-simple-yet-effective","title":"SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation","date":"2022-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucdvision/simreg","path":"models/resnet_byol.py","file_url":"https://github.com/ucdvision/simreg/blob/HEAD/models/resnet_byol.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4b3a9d234aede9e","mcp_get_code":{"code_sha256":"c4b3a9d234aede9e"}},{"arxiv_id":"2201.01293","paper":"/paper/a-transformer-based-siamese-network-for","title":"A Transformer-Based Siamese Network for Change Detection","date":"2022-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/changeformer","path":"models/DTCDSCN.py","file_url":"https://github.com/wgcban/changeformer/blob/HEAD/models/DTCDSCN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2112.13762","paper":"/paper/mseg-a-composite-dataset-for-multi-domain-1","title":"MSeg: A Composite Dataset for Multi-domain Semantic Segmentation","date":"2021-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mseg-dataset/mseg-semantic","path":"mseg_semantic/model/resnet.py","file_url":"https://github.com/mseg-dataset/mseg-semantic/blob/HEAD/mseg_semantic/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.13762","paper":"/paper/mseg-a-composite-dataset-for-multi-domain-1","title":"MSeg: A Composite Dataset for Multi-domain Semantic Segmentation","date":"2021-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mseg-dataset/mseg-semantic","path":"mseg_semantic/model/seg_hrnet.py","file_url":"https://github.com/mseg-dataset/mseg-semantic/blob/HEAD/mseg_semantic/model/seg_hrnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1934d9a8c4ffcd55","mcp_get_code":{"code_sha256":"1934d9a8c4ffcd55"}},{"arxiv_id":"2112.13692","paper":"/paper/augmenting-convolutional-networks-with","title":"Augmenting Convolutional networks with attention-based aggregation","date":"2021-12-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/deit","path":"patchconvnet_models.py","file_url":"https://github.com/facebookresearch/deit/blob/HEAD/patchconvnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ba6aa5f07daca9cd","mcp_get_code":{"code_sha256":"ba6aa5f07daca9cd"}},{"arxiv_id":"2112.11088","paper":"/paper/epnet-cascade-bi-directional-fusion-for-multi","title":"EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection","date":"2021-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"happinesslz/epnetv2","path":"lib/net/pointnet2_msg.py","file_url":"https://github.com/happinesslz/epnetv2/blob/HEAD/lib/net/pointnet2_msg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.09130","paper":"/paper/ensembling-off-the-shelf-models-for-gan","title":"Ensembling Off-the-shelf Models for GAN Training","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nupurkmr9/vision-aided-gan","path":"vision_aided_loss/resnet.py","file_url":"https://github.com/nupurkmr9/vision-aided-gan/blob/HEAD/vision_aided_loss/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.07368","paper":"/paper/simple-and-robust-loss-design-for-multi-label","title":"Simple and Robust Loss Design for Multi-Label Learning with Missing Labels","date":"2021-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinyu1205/robust-loss-mlml","path":"src/models/resnet.py","file_url":"https://github.com/xinyu1205/robust-loss-mlml/blob/HEAD/src/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c10ec4100ad0c47","mcp_get_code":{"code_sha256":"3c10ec4100ad0c47"}},{"arxiv_id":"2112.07133","paper":"/paper/clip-lite-information-efficient-visual","title":"CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision","date":"2021-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"4m4n5/CLIP-Lite","path":"model_zoo/resnet.py","file_url":"https://github.com/4m4n5/CLIP-Lite/blob/HEAD/model_zoo/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.06183","paper":"/paper/few-shot-keypoint-detection-with-uncertainty","title":"Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species","date":"2021-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alanlusun/few-shot-keypoint-detection","path":"network/pose_hrnet.py","file_url":"https://github.com/alanlusun/few-shot-keypoint-detection/blob/HEAD/network/pose_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.05637","paper":"/paper/headnerf-a-real-time-nerf-based-parametric","title":"HeadNeRF: A Real-time NeRF-based Parametric Head Model","date":"2021-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crishy1995/headnerf","path":"DataProcess/resnet.py","file_url":"https://github.com/crishy1995/headnerf/blob/HEAD/DataProcess/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.05150","paper":"/paper/deep-recurrent-neural-network-with-multi","title":"Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xjtu-cvlab-lowlevel/rnn-mbp","path":"model/arches.py","file_url":"https://github.com/xjtu-cvlab-lowlevel/rnn-mbp/blob/HEAD/model/arches.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2112.05134","paper":"/paper/a-shared-representation-for-photorealistic","title":"A Shared Representation for Photorealistic Driving Simulators","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-epfl/semdisc","path":"models/networks/resnet.py","file_url":"https://github.com/vita-epfl/semdisc/blob/HEAD/models/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.05006","paper":"/paper/exploring-event-driven-dynamic-context-for","title":"Exploring Event-driven Dynamic Context for Accident Scene Segmentation","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamycheung/ISSAFE","path":"models/edcnet.py","file_url":"https://github.com/jamycheung/ISSAFE/blob/HEAD/models/edcnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"48468b2c75aae583","mcp_get_code":{"code_sha256":"48468b2c75aae583"}},{"arxiv_id":"2112.04628","paper":"/paper/learning-auxiliary-monocular-contexts-helps","title":"Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xianpeng919/MonoCon","path":"monocon/mmdet3d/models/backbones/dla.py","file_url":"https://github.com/Xianpeng919/MonoCon/blob/HEAD/monocon/mmdet3d/models/backbones/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.04564","paper":"/paper/cossl-co-learning-of-representation-and","title":"CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning","date":"2021-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yue-fan/cossl","path":"models/wrn.py","file_url":"https://github.com/yue-fan/cossl/blob/HEAD/models/wrn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4917ae1314a3442","mcp_get_code":{"code_sha256":"a4917ae1314a3442"}},{"arxiv_id":"2112.03731","paper":"/paper/salfbnet-learning-pseudo-saliency","title":"SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks","date":"2021-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gqding/salfbnet","path":"networks/resnetfixed.py","file_url":"https://github.com/gqding/salfbnet/blob/HEAD/networks/resnetfixed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2112.02753","paper":"/paper/mobrecon-mobile-friendly-hand-mesh","title":"MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image","date":"2021-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeanChenxy/HandMesh","path":"cmr/models/resnet.py","file_url":"https://github.com/SeanChenxy/HandMesh/blob/HEAD/cmr/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2112.00337","paper":"/paper/a-unified-benchmark-for-the-unknown-detection","title":"A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daintlab/unknown-detection-benchmarks","path":"models/resnet_imagenet.py","file_url":"https://github.com/daintlab/unknown-detection-benchmarks/blob/HEAD/models/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2112.00337","paper":"/paper/a-unified-benchmark-for-the-unknown-detection","title":"A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daintlab/unknown-detection-benchmarks","path":"models/resnet_cifar.py","file_url":"https://github.com/daintlab/unknown-detection-benchmarks/blob/HEAD/models/resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2112.00059","paper":"/paper/evaluating-gradient-inversion-attacks-and-1","title":"Evaluating Gradient Inversion Attacks and Defenses in Federated Learning","date":"2021-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Princeton-SysML/GradAttack","path":"gradattack/models/multihead_resnet.py","file_url":"https://github.com/Princeton-SysML/GradAttack/blob/HEAD/gradattack/models/multihead_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.15000","paper":"/paper/deformable-protopnet-an-interpretable-image","title":"Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jdonnelly36/Deformable-ProtoPNet","path":"resnet_features.py","file_url":"https://github.com/jdonnelly36/Deformable-ProtoPNet/blob/HEAD/resnet_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.14556","paper":"/paper/on-the-integration-of-self-attention-and","title":"On the Integration of Self-Attention and Convolution","date":"2021-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2111.12853","paper":"/paper/amortized-prompt-lightweight-fine-tuning-for","title":"Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains","date":"2021-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shogi880/DPLCLIP","path":"domainbed/lib/big_transfer.py","file_url":"https://github.com/shogi880/DPLCLIP/blob/HEAD/domainbed/lib/big_transfer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2111.11986","paper":"/paper/hero-hessian-enhanced-robust-optimization-for","title":"HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance","date":"2021-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiaoxuan-Yang/HERO","path":"models/cifar/preresnet.py","file_url":"https://github.com/Xiaoxuan-Yang/HERO/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.11326","paper":"/paper/dytox-transformers-for-continual-learning","title":"DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion","date":"2021-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arthurdouillard/dytox","path":"continual/cnn/resnet.py","file_url":"https://github.com/arthurdouillard/dytox/blob/HEAD/continual/cnn/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2111.11326","paper":"/paper/dytox-transformers-for-continual-learning","title":"DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion","date":"2021-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arthurdouillard/dytox","path":"continual/cnn/resnet_scs.py","file_url":"https://github.com/arthurdouillard/dytox/blob/HEAD/continual/cnn/resnet_scs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"d4448dd8427a42bd","mcp_get_code":{"code_sha256":"d4448dd8427a42bd"}},{"arxiv_id":"2111.09805","paper":"/paper/on-the-effectiveness-of-sparsification-for","title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deeplearning-wisc/dice","path":"models/resnet.py","file_url":"https://github.com/deeplearning-wisc/dice/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.09805","paper":"/paper/on-the-effectiveness-of-sparsification-for","title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deeplearning-wisc/dice","path":"models/resnetv2.py","file_url":"https://github.com/deeplearning-wisc/dice/blob/HEAD/models/resnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2111.05956","paper":"/paper/feature-generation-for-long-tail","title":"Feature Generation for Long-tail Classification","date":"2021-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rahulvigneswaran/tailcalibx","path":"libs/models/ResNextFeature.py","file_url":"https://github.com/rahulvigneswaran/tailcalibx/blob/HEAD/libs/models/ResNextFeature.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.05846","paper":"/paper/structure-from-silence-learning-scene","title":"Structure from Silence: Learning Scene Structure from Ambient Sound","date":"2021-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IFICL/structure-from-silence","path":"models/resnet.py","file_url":"https://github.com/IFICL/structure-from-silence/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.05177","paper":"/paper/on-training-implicit-models","title":"On Training Implicit Models","date":"2021-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsunshine/phantom_grad","path":"MDEQ/MDEQ_ImageNet/models/mdeq.py","file_url":"https://github.com/gsunshine/phantom_grad/blob/HEAD/MDEQ/MDEQ_ImageNet/models/mdeq.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"2111.04578","paper":"/paper/improved-regularization-and-robustness-for","title":"Improved Regularization and Robustness for Fine-tuning in Neural Networks","date":"2021-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VirtuosoResearch/Regularized-Self-Labeling","path":"model/modeling_resnet.py","file_url":"https://github.com/VirtuosoResearch/Regularized-Self-Labeling/blob/HEAD/model/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2111.04316","paper":"/paper/sega-semantic-guided-attention-on-visual","title":"SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning","date":"2021-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martayang/sega","path":"models/ResNet12_embedding.py","file_url":"https://github.com/martayang/sega/blob/HEAD/models/ResNet12_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.03216","paper":"/paper/fast-camouflaged-object-detection-via-edge","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","date":"2021-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gewelsji/errnet","path":"model/resnet.py","file_url":"https://github.com/gewelsji/errnet/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2111.01124","paper":"/paper/when-does-contrastive-learning-preserve","title":"When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?","date":"2021-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LijieFan/AdvCL","path":"models/resnet_cifar_multibn_ensembleFC.py","file_url":"https://github.com/LijieFan/AdvCL/blob/HEAD/models/resnet_cifar_multibn_ensembleFC.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2111.00210","paper":"/paper/mastering-atari-games-with-limited-data","title":"Mastering Atari Games with Limited Data","date":"2021-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"werner-duvaud/muzero-general","path":"models.py","file_url":"https://github.com/werner-duvaud/muzero-general/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dad29e46e1d9e83","mcp_get_code":{"code_sha256":"2dad29e46e1d9e83"}},{"arxiv_id":"2110.14807","paper":"/paper/l2ight-enabling-on-chip-learning-for-optical","title":"L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization","date":"2021-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeremiemelo/l2ight","path":"core/models/sparse_bp_resnet.py","file_url":"https://github.com/jeremiemelo/l2ight/blob/HEAD/core/models/sparse_bp_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"94803c3f63d60a8c","mcp_get_code":{"code_sha256":"94803c3f63d60a8c"}},{"arxiv_id":"2110.14577","paper":"/paper/a-geometric-perspective-towards-neural","title":"A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition","date":"2021-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gt-ripl/geometric-sensitivity-decomposition","path":"models/wide_resnet.py","file_url":"https://github.com/gt-ripl/geometric-sensitivity-decomposition/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dc0af2f648f31d69","mcp_get_code":{"code_sha256":"dc0af2f648f31d69"}},{"arxiv_id":"2110.11499","paper":"/paper/wav2clip-learning-robust-audio","title":"Wav2CLIP: Learning Robust Audio Representations From CLIP","date":"2021-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"descriptinc/lyrebird-wav2clip","path":"wav2clip/model/resnet.py","file_url":"https://github.com/descriptinc/lyrebird-wav2clip/blob/HEAD/wav2clip/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2110.08733","paper":"/paper/loveda-a-remote-sensing-land-cover-dataset","title":"LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation","date":"2021-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Luffy03/DCA","path":"module/_resnets.py","file_url":"https://github.com/Luffy03/DCA/blob/HEAD/module/_resnets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2110.07607","paper":"/paper/humbugdb-a-large-scale-acoustic-mosquito","title":"HumBugDB: A Large-scale Acoustic Mosquito Dataset","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HumBug-Mosquito/HumBugDB","path":"lib/PyTorch/ResNetDropoutSource.py","file_url":"https://github.com/HumBug-Mosquito/HumBugDB/blob/HEAD/lib/PyTorch/ResNetDropoutSource.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2110.07402","paper":"/paper/self-supervised-learning-by-estimating-twin-1","title":"Self-Supervised Learning by Estimating Twin Class Distributions","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/TWIST","path":"widen_resnet.py","file_url":"https://github.com/bytedance/TWIST/blob/HEAD/widen_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2110.06923","paper":"/paper/object-dgcnn-3d-object-detection-using","title":"Object DGCNN: 3D Object Detection using Dynamic Graphs","date":"2021-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangyueft/detr3d","path":"projects/mmdet3d_plugin/models/backbones/vovnet.py","file_url":"https://github.com/wangyueft/detr3d/blob/HEAD/projects/mmdet3d_plugin/models/backbones/vovnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"303f4d3695ad18f1","mcp_get_code":{"code_sha256":"303f4d3695ad18f1"}},{"arxiv_id":"2110.06448","paper":"/paper/reducing-the-covariate-shift-by-mirror","title":"Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment","date":"2021-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cti-vision/mirror-sample","path":"Models/Mirror_Model.py","file_url":"https://github.com/cti-vision/mirror-sample/blob/HEAD/Models/Mirror_Model.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"904acc95bc6b73ea","mcp_get_code":{"code_sha256":"904acc95bc6b73ea"}},{"arxiv_id":"2110.05283","paper":"/paper/phase-collapse-in-neural-networks-1","title":"Phase Collapse in Neural Networks","date":"2021-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"florentinguth/phasecollapse","path":"models/resnet.py","file_url":"https://github.com/florentinguth/phasecollapse/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2110.03593","paper":"/paper/transalnet-visual-saliency-prediction-using","title":"TranSalNet: Towards perceptually relevant visual saliency prediction","date":"2021-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ljovo/transalnet","path":"utils/resnet.py","file_url":"https://github.com/ljovo/transalnet/blob/HEAD/utils/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2110.00990","paper":"/paper/hierarchical-kinematic-probability","title":"Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild","date":"2021-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akashsengupta1997/hierarchicalprobabilistic3dhuman","path":"models/poseMF_shapeGaussian_net.py","file_url":"https://github.com/akashsengupta1997/hierarchicalprobabilistic3dhuman/blob/HEAD/models/poseMF_shapeGaussian_net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76fdd94bb075ca26","mcp_get_code":{"code_sha256":"76fdd94bb075ca26"}},{"arxiv_id":"2109.15222","paper":"/paper/self-supervised-out-of-distribution-detection-1","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","date":"2021-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmsch/natural-synthetic-anomalies","path":"model/resnet.py","file_url":"https://github.com/hmsch/natural-synthetic-anomalies/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2109.14120","paper":"/paper/meta-learning-on-a-sequence-of-imbalanced","title":"Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/imbalancemeta","path":"net/resnet.py","file_url":"https://github.com/joey-wang123/imbalancemeta/blob/HEAD/net/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2109.14120","paper":"/paper/meta-learning-on-a-sequence-of-imbalanced","title":"Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/imbalancemeta","path":"model.py","file_url":"https://github.com/joey-wang123/imbalancemeta/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a96935ee89a7588f","mcp_get_code":{"code_sha256":"a96935ee89a7588f"}},{"arxiv_id":"2109.14120","paper":"/paper/meta-learning-on-a-sequence-of-imbalanced","title":"Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness","date":"2021-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/imbalancemeta","path":"net/convnet.py","file_url":"https://github.com/joey-wang123/imbalancemeta/blob/HEAD/net/convnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0277eda759fbe6db","mcp_get_code":{"code_sha256":"0277eda759fbe6db"}},{"arxiv_id":"2109.07839","paper":"/paper/self-supervised-contrastive-learning-for-eeg","title":"Self-supervised Contrastive Learning for EEG-based Sleep Staging","date":"2021-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuejiang16/ssl-torch","path":"net.py","file_url":"https://github.com/xuejiang16/ssl-torch/blob/HEAD/net.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b87ab6dcf412f29c","mcp_get_code":{"code_sha256":"b87ab6dcf412f29c"}},{"arxiv_id":"2109.04153","paper":"/paper/single-image-3d-object-estimation-with","title":"Single Image 3D Object Estimation with Primitive Graph Networks","date":"2021-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hailieqh/3d-object-primitive-graph","path":"3dprnn_pytorch/lib/models/bbox_model.py","file_url":"https://github.com/hailieqh/3d-object-primitive-graph/blob/HEAD/3dprnn_pytorch/lib/models/bbox_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2109.03229","paper":"/paper/rethinking-common-assumptions-to-mitigate","title":"Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets","date":"2021-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"j-alex-hanson/rethinking-race-face-datasets","path":"models/resnet.py","file_url":"https://github.com/j-alex-hanson/rethinking-race-face-datasets/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2109.01801","paper":"/paper/dual-transfer-learning-for-event-based-end","title":"Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation","date":"2021-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"addisonwang2013/dtl","path":"backbone/drn.py","file_url":"https://github.com/addisonwang2013/dtl/blob/HEAD/backbone/drn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"2109.00150","paper":"/paper/federated-reconnaissance-efficient","title":"Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning","date":"2021-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml4ai/fed-recon","path":"fed_recon/models/protonet/model.py","file_url":"https://github.com/ml4ai/fed-recon/blob/HEAD/fed_recon/models/protonet/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a96935ee89a7588f","mcp_get_code":{"code_sha256":"a96935ee89a7588f"}},{"arxiv_id":"2109.00150","paper":"/paper/federated-reconnaissance-efficient","title":"Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning","date":"2021-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml4ai/fed-recon","path":"fed_recon/models/gradient_based/protonet_cnn.py","file_url":"https://github.com/ml4ai/fed-recon/blob/HEAD/fed_recon/models/gradient_based/protonet_cnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db298d586485a5d5","mcp_get_code":{"code_sha256":"db298d586485a5d5"}},{"arxiv_id":"2108.12510","paper":"/paper/pulling-up-by-the-causal-bootstraps-causal","title":"Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing","date":"2021-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MLforHealth/CausalDA","path":"model/resnet_multispectral.py","file_url":"https://github.com/MLforHealth/CausalDA/blob/HEAD/model/resnet_multispectral.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2108.12141","paper":"/paper/dae-gan-dynamic-aspect-aware-gan-for-text-to","title":"DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis","date":"2021-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hiarsal/DAE-GAN","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d33e18153be6aeed","mcp_get_code":{"code_sha256":"d33e18153be6aeed"}},{"arxiv_id":"2108.10612","paper":"/paper/protomil-multiple-instance-learning-with","title":"ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification","date":"2021-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apardyl/protomil","path":"base_models/resnet_features.py","file_url":"https://github.com/apardyl/protomil/blob/HEAD/base_models/resnet_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2108.08367","paper":"/paper/so-pose-exploiting-self-occlusion-for-direct","title":"SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation","date":"2021-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-DA-6D-Pose-Group/GDR-Net","path":"core/gdrn_modeling/models/pvnet_net/resnet.py","file_url":"https://github.com/THU-DA-6D-Pose-Group/GDR-Net/blob/HEAD/core/gdrn_modeling/models/pvnet_net/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"861bfac8e714414f","mcp_get_code":{"code_sha256":"861bfac8e714414f"}},{"arxiv_id":"2108.08165","paper":"/paper/generalized-and-incremental-few-shot-learning","title":"Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting","date":"2021-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"annusha/lcwof","path":"mini_imgnet/resnet12.py","file_url":"https://github.com/annusha/lcwof/blob/HEAD/mini_imgnet/resnet12.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2108.06552","paper":"/paper/weakly-supervised-continual-learning","title":"Continual Semi-Supervised Learning through Contrastive Interpolation Consistency","date":"2021-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"loribonna/cssl","path":"backbone/ResNet18.py","file_url":"https://github.com/loribonna/cssl/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2108.05793","paper":"/paper/progressive-coordinate-transforms-for","title":"Progressive Coordinate Transforms for Monocular 3D Object Detection","date":"2021-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/progressive-coordinate-transforms","path":"lib/backbones/modeling_resnet.py","file_url":"https://github.com/amazon-research/progressive-coordinate-transforms/blob/HEAD/lib/backbones/modeling_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2108.05722","paper":"/paper/mt-orl-multi-task-occlusion-relationship","title":"MT-ORL: Multi-Task Occlusion Relationship Learning","date":"2021-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fengpanhe/MT-ORL","path":"mtorl/models/opnet.py","file_url":"https://github.com/fengpanhe/MT-ORL/blob/HEAD/mtorl/models/opnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"2108.05293","paper":"/paper/few-shot-segmentation-with-global-and-local","title":"Few-Shot Segmentation with Global and Local Contrastive Learning","date":"2021-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuweide01/GQNet-Few-shot-segmentation","path":"model/resnet.py","file_url":"https://github.com/liuweide01/GQNet-Few-shot-segmentation/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2108.05009","paper":"/paper/learning-deep-multimodal-feature","title":"Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion","date":"2021-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yikaiw/AsymFusion","path":"models/model.py","file_url":"https://github.com/yikaiw/AsymFusion/blob/HEAD/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c930e46d06bfa25","mcp_get_code":{"code_sha256":"7c930e46d06bfa25"}},{"arxiv_id":"2108.04584","paper":"/paper/uninet-a-unified-scene-understanding-network","title":"UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks","date":"2021-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NeurAI-Lab/UniNet","path":"encoding_custom/backbones/vovnet.py","file_url":"https://github.com/NeurAI-Lab/UniNet/blob/HEAD/encoding_custom/backbones/vovnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d1fbadb5d1136a50","mcp_get_code":{"code_sha256":"d1fbadb5d1136a50"}},{"arxiv_id":"2108.03690","paper":"/paper/enhanced-invertible-encoding-for-learned","title":"Enhanced Invertible Encoding for Learned Image Compression","date":"2021-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyq7/invcompress","path":"codes/compressai/layers/layers.py","file_url":"https://github.com/xyq7/invcompress/blob/HEAD/codes/compressai/layers/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fce10771ab1d00db","mcp_get_code":{"code_sha256":"fce10771ab1d00db"}},{"arxiv_id":"2108.02833","paper":"/paper/elaborative-rehearsal-for-zero-shot-action","title":"Elaborative Rehearsal for Zero-shot Action Recognition","date":"2021-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeLightCMU/ElaborativeRehearsal","path":"bit_pytorch/models.py","file_url":"https://github.com/DeLightCMU/ElaborativeRehearsal/blob/HEAD/bit_pytorch/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2108.02722","paper":"/paper/video-contrastive-learning-with-global","title":"Video Contrastive Learning with Global Context","date":"2021-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/video-contrastive-learning","path":"models/resnet_mlp.py","file_url":"https://github.com/amazon-research/video-contrastive-learning/blob/HEAD/models/resnet_mlp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2108.01684","paper":"/paper/vision-transformer-with-progressive-sampling","title":"Vision Transformer with Progressive Sampling","date":"2021-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuexy/PS-ViT","path":"models/ps_vit.py","file_url":"https://github.com/yuexy/PS-ViT/blob/HEAD/models/ps_vit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2108.00352","paper":"/paper/badencoder-backdoor-attacks-to-pre-trained","title":"BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning","date":"2021-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liu00222/StolenEncoder","path":"CLIP/models/imagenet_model.py","file_url":"https://github.com/liu00222/StolenEncoder/blob/HEAD/CLIP/models/imagenet_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2108.00351","paper":"/paper/lasor-learning-accurate-3d-human-pose-and","title":"LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering","date":"2021-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iGame-Lab/LASOR","path":"models/resnet.py","file_url":"https://github.com/iGame-Lab/LASOR/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2107.13774","paper":"/paper/geometry-uncertainty-projection-network-for","title":"Geometry Uncertainty Projection Network for Monocular 3D Object Detection","date":"2021-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"supermhp/gupnet","path":"code/lib/backbones/dla.py","file_url":"https://github.com/supermhp/gupnet/blob/HEAD/code/lib/backbones/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2107.13774","paper":"/paper/geometry-uncertainty-projection-network-for","title":"Geometry Uncertainty Projection Network for Monocular 3D Object Detection","date":"2021-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"supermhp/gupnet","path":"code/lib/backbones/resnet.py","file_url":"https://github.com/supermhp/gupnet/blob/HEAD/code/lib/backbones/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c875390a9fab8007","mcp_get_code":{"code_sha256":"c875390a9fab8007"}},{"arxiv_id":"2107.13335","paper":"/paper/wavecnet-wavelet-integrated-cnns-to-suppress","title":"WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification","date":"2021-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiQiufu/WaveCNet","path":"models_dwt/resnet.py","file_url":"https://github.com/LiQiufu/WaveCNet/blob/HEAD/models_dwt/resnet.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"df92f1a5ba14210a","mcp_get_code":{"code_sha256":"df92f1a5ba14210a"}},{"arxiv_id":"2107.13098","paper":"/paper/a-tale-of-two-long-tails","title":"A Tale Of Two Long Tails","date":"2021-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dsouzadaniel/long_tail","path":"models/wide_resnet.py","file_url":"https://github.com/dsouzadaniel/long_tail/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2107.12571","paper":"/paper/cflow-ad-real-time-unsupervised-anomaly","title":"CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows","date":"2021-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gudovskiy/cflow-ad","path":"custom_models/resnet.py","file_url":"https://github.com/gudovskiy/cflow-ad/blob/HEAD/custom_models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"436bb4b1f6464d1b","mcp_get_code":{"code_sha256":"436bb4b1f6464d1b"}},{"arxiv_id":"2107.11673","paper":"/paper/scalehls-scalable-high-level-synthesis","title":"ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation","date":null,"month_inferred_from_arxiv_id":"2021-07","title_source":"archive","repo":"hanchenye/scalehls","path":"samples/pytorch/resnet18/resnet18.py","file_url":"https://github.com/hanchenye/scalehls/blob/HEAD/samples/pytorch/resnet18/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2107.08621","paper":"/paper/face-evolve-a-high-performance-face","title":"Face.evoLVe: A High-Performance Face Recognition Library","date":"2021-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhaoJ9014/face.evoLVe.PyTorch","path":"backbone/model_resnet.py","file_url":"https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/blob/HEAD/backbone/model_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"999e0ea90670a179","mcp_get_code":{"code_sha256":"999e0ea90670a179"}},{"arxiv_id":"2107.07110","paper":"/paper/recurrent-parameter-generators","title":"Compact and Optimal Deep Learning with Recurrent Parameter Generators","date":"2021-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samaonline/Recurrent-Parameter-Generators","path":"supermodels/super_resnet_equal.py","file_url":"https://github.com/samaonline/Recurrent-Parameter-Generators/blob/HEAD/supermodels/super_resnet_equal.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2107.04941","paper":"/paper/partial-video-domain-adaptation-with-partial","title":"Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network","date":"2021-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuyu0010/PATAN","path":"network/util.py","file_url":"https://github.com/xuyu0010/PATAN/blob/HEAD/network/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77f89e05c55c985d","mcp_get_code":{"code_sha256":"77f89e05c55c985d"}},{"arxiv_id":"2107.02368","paper":"/paper/uacanet-uncertainty-augmented-context","title":"UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plemeri/UACANet","path":"lib/backbones/ResNet.py","file_url":"https://github.com/plemeri/UACANet/blob/HEAD/lib/backbones/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.16028","paper":"/paper/efficient-spatio-temporal-recurrent-neural-1","title":"Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network","date":"2021-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzh-tech/ESTRNN","path":"model/arches.py","file_url":"https://github.com/zzh-tech/ESTRNN/blob/HEAD/model/arches.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2106.14616","paper":"/paper/icdar-2021-competition-on-scientific","title":"ICDAR 2021 Competition on Scientific Literature Parsing","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenwenyu/MASTER-pytorch","path":"model/backbone.py","file_url":"https://github.com/wenwenyu/MASTER-pytorch/blob/HEAD/model/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec20f22a185cd708","mcp_get_code":{"code_sha256":"ec20f22a185cd708"}},{"arxiv_id":"2106.13358","paper":"/paper/scalable-perception-action-communication","title":"Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks","date":"2021-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/VGAI","path":"joint_network.py","file_url":"https://github.com/VITA-Group/VGAI/blob/HEAD/joint_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.09681","paper":"/paper/xcit-cross-covariance-image-transformers","title":"XCiT: Cross-Covariance Image Transformers","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/xcit","path":"xcit.py","file_url":"https://github.com/facebookresearch/xcit/blob/HEAD/xcit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"429abe9dc8793925","mcp_get_code":{"code_sha256":"429abe9dc8793925"}},{"arxiv_id":"2106.09563","paper":"/paper/on-anytime-learning-at-macroscale","title":"On Anytime Learning at Macroscale","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/alma","path":"crlapi/sl/architectures/resnet.py","file_url":"https://github.com/facebookresearch/alma/blob/HEAD/crlapi/sl/architectures/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"ec20f22a185cd708","mcp_get_code":{"code_sha256":"ec20f22a185cd708"}},{"arxiv_id":"2106.09352","paper":"/paper/large-scale-private-learning-via-low-rank","title":"Large Scale Private Learning via Low-rank Reparametrization","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dayu11/Differentially-Private-Deep-Learning","path":"vision/RGP/models/resnet_cifar.py","file_url":"https://github.com/dayu11/Differentially-Private-Deep-Learning/blob/HEAD/vision/RGP/models/resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e6cd72f51ebc400","mcp_get_code":{"code_sha256":"6e6cd72f51ebc400"}},{"arxiv_id":"2106.09017","paper":"/paper/bridging-multi-task-learning-and-meta","title":"Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation","date":"2021-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI-secure/multi-task-learning","path":"models/resnet.py","file_url":"https://github.com/AI-secure/multi-task-learning/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.08265","paper":"/paper/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tbcvContributor/DeepHawkeye","path":"src/wide_res_model.py","file_url":"https://github.com/tbcvContributor/DeepHawkeye/blob/HEAD/src/wide_res_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2106.07849","paper":"/paper/simon-says-evaluating-and-mitigating-bias-in","title":"Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"codestar12/pruning-distilation-bias","path":"models/resnet.py","file_url":"https://github.com/codestar12/pruning-distilation-bias/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.06984","paper":"/paper/a-free-lunch-from-ann-towards-efficient","title":"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhhhli/SNN_Calibration","path":"models/CIFAR/models/resnet.py","file_url":"https://github.com/yhhhli/SNN_Calibration/blob/HEAD/models/CIFAR/models/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dacc6812ec177540","mcp_get_code":{"code_sha256":"dacc6812ec177540"}},{"arxiv_id":"2106.06984","paper":"/paper/a-free-lunch-from-ann-towards-efficient","title":"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhhhli/SNN_Calibration","path":"models/ImageNet/models/resnet.py","file_url":"https://github.com/yhhhli/SNN_Calibration/blob/HEAD/models/ImageNet/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ab65b82589cc9e7","mcp_get_code":{"code_sha256":"1ab65b82589cc9e7"}},{"arxiv_id":"2106.06560","paper":"/paper/hr-nas-searching-efficient-high-resolution","title":"HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/HR-NAS","path":"models/hrnet_base.py","file_url":"https://github.com/dingmyu/HR-NAS/blob/HEAD/models/hrnet_base.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.06042","paper":"/paper/fedbabu-towards-enhanced-representation-for","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhoon-oh/fedbabu","path":"models/Nets.py","file_url":"https://github.com/jhoon-oh/fedbabu/blob/HEAD/models/Nets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7eccbc3004b9fde7","mcp_get_code":{"code_sha256":"7eccbc3004b9fde7"}},{"arxiv_id":"2106.05095","paper":"/paper/st-make-self-training-work-better-for-semi","title":"ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiheYoung/ST-PlusPlus","path":"model/backbone/resnet.py","file_url":"https://github.com/LiheYoung/ST-PlusPlus/blob/HEAD/model/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"794a2ed91a5175bd","mcp_get_code":{"code_sha256":"794a2ed91a5175bd"}},{"arxiv_id":"2106.04732","paper":"/paper/adamatch-a-unified-approach-to-semi","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smkim7-kr/AdaMatch-pytorch","path":"models.py","file_url":"https://github.com/smkim7-kr/AdaMatch-pytorch/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2106.04570","paper":"/paper/meta-learning-for-knowledge-distillation","title":"BERT Learns to Teach: Knowledge Distillation with Meta Learning","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JetRunner/MetaDistil","path":"cv/models/resnet.py","file_url":"https://github.com/JetRunner/MetaDistil/blob/HEAD/cv/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.04570","paper":"/paper/meta-learning-for-knowledge-distillation","title":"BERT Learns to Teach: Knowledge Distillation with Meta Learning","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JetRunner/MetaDistil","path":"cv/models/meta_resnet.py","file_url":"https://github.com/JetRunner/MetaDistil/blob/HEAD/cv/models/meta_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0c5a648a4b38bad","mcp_get_code":{"code_sha256":"f0c5a648a4b38bad"}},{"arxiv_id":"2106.04550","paper":"/paper/detreg-unsupervised-pretraining-with-region","title":"DETReg: Unsupervised Pretraining with Region Priors for Object Detection","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirbar/detreg","path":"models/swav_resnet50.py","file_url":"https://github.com/amirbar/detreg/blob/HEAD/models/swav_resnet50.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2106.03225","paper":"/paper/efficient-lottery-ticket-finding-less-data-is","title":"Efficient Lottery Ticket Finding: Less Data is More","date":"2021-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/PrAC-LTH","path":"models/resnet.py","file_url":"https://github.com/VITA-Group/PrAC-LTH/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2106.02933","paper":"/paper/k-mixup-regularization-for-deep-learning-via","title":"k-Mixup Regularization for Deep Learning via Optimal Transport","date":"2021-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anminggu/kmixup-cifar10","path":"models/resnet.py","file_url":"https://github.com/anminggu/kmixup-cifar10/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2106.02874","paper":"/paper/rda-robust-domain-adaptation-via-fourier","title":"RDA: Robust Domain Adaptation via Fourier Adversarial Attacking","date":"2021-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jxhuang0508/RDA","path":"crst/deeplab/model_advent.py","file_url":"https://github.com/jxhuang0508/RDA/blob/HEAD/crst/deeplab/model_advent.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.02637","paper":"/paper/aligning-pretraining-for-detection-via-object","title":"Aligning Pretraining for Detection via Object-Level Contrastive Learning","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hologerry/SoCo","path":"contrast/resnet.py","file_url":"https://github.com/hologerry/SoCo/blob/HEAD/contrast/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.02266","paper":"/paper/sand-mask-an-enhanced-gradient-masking","title":"SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shahtalebi/SAND-mask","path":"domainbed/lib/wide_resnet.py","file_url":"https://github.com/shahtalebi/SAND-mask/blob/HEAD/domainbed/lib/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2106.01226","paper":"/paper/semi-supervised-semantic-segmentation-with-3","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"charlesCXK/TorchSemiSeg","path":"furnace/base_model/hrnet.py","file_url":"https://github.com/charlesCXK/TorchSemiSeg/blob/HEAD/furnace/base_model/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2106.01127","paper":"/paper/towards-robust-classification-model-by","title":"Towards Robust Classification Model by Counterfactual and Invariant Data Generation","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzzace2000/robust_cls_model","path":"arch/models.py","file_url":"https://github.com/zzzace2000/robust_cls_model/blob/HEAD/arch/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2106.00209","paper":"/paper/rethinking-re-sampling-in-imbalanced-semi","title":"Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning","date":"2021-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TACJu/Bi-Sampling","path":"models/wrn.py","file_url":"https://github.com/TACJu/Bi-Sampling/blob/HEAD/models/wrn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4917ae1314a3442","mcp_get_code":{"code_sha256":"a4917ae1314a3442"}},{"arxiv_id":"2105.14953","paper":"/paper/ace-node-attentive-co-evolving-neural","title":"ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations","date":"2021-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.14148","paper":"/paper/openmatch-open-set-consistency-regularization","title":"OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/OP_Match","path":"models/resnet_imagenet.py","file_url":"https://github.com/VisionLearningGroup/OP_Match/blob/HEAD/models/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2105.14083","paper":"/paper/rethinking-noisy-label-models-labeler","title":"Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiJunnan1992/DivideMix","path":"PreResNet.py","file_url":"https://github.com/LiJunnan1992/DivideMix/blob/HEAD/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2105.10793","paper":"/paper/goo-a-dataset-for-gaze-object-prediction-in","title":"GOO: A Dataset for Gaze Object Prediction in Retail Environments","date":"2021-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"upeee/GOO-GAZE2021","path":"gazefollowing/models/resnet.py","file_url":"https://github.com/upeee/GOO-GAZE2021/blob/HEAD/gazefollowing/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.10123","paper":"/paper/backdoor-attacks-on-self-supervised-learning","title":"Backdoor Attacks on Self-Supervised Learning","date":"2021-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UMBCvision/SSL-Backdoor","path":"jigsaw/resnet.py","file_url":"https://github.com/UMBCvision/SSL-Backdoor/blob/HEAD/jigsaw/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2105.10026","paper":"/paper/improving-generation-and-evaluation-of-visual","title":"Improving Generation and Evaluation of Visual Stories via Semantic Consistency","date":"2021-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adymaharana/StoryViz","path":"dcsgan/model.py","file_url":"https://github.com/adymaharana/StoryViz/blob/HEAD/dcsgan/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.09109","paper":"/paper/an-orthogonal-classifier-for-improving-the","title":"An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks","date":"2021-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MTandHJ/roboc","path":"models/resnet.py","file_url":"https://github.com/MTandHJ/roboc/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3600f032ac79fc6b","mcp_get_code":{"code_sha256":"3600f032ac79fc6b"}},{"arxiv_id":"2105.06152","paper":"/paper/when-human-pose-estimation-meets-robustness","title":"When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HRNet/HigherHRNet-Human-Pose-Estimation","path":"lib/models/pose_higher_hrnet.py","file_url":"https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation/blob/HEAD/lib/models/pose_higher_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.03245","paper":"/paper/adaptive-focus-for-efficient-video","title":"Adaptive Focus for Efficient Video Recognition","date":"2021-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blackfeather-wang/AdaFocus","path":"Experiments on ActivityNet, FCVID and Mini-Kinetics/models/gfv_net.py","file_url":"https://github.com/blackfeather-wang/AdaFocus/blob/HEAD/Experiments%20on%20ActivityNet%2C%20FCVID%20and%20Mini-Kinetics/models/gfv_net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"29b46555d311b33f","mcp_get_code":{"code_sha256":"29b46555d311b33f"}},{"arxiv_id":"2105.03193","paper":"/paper/network-pruning-that-matters-a-case-study-on-1","title":"Network Pruning That Matters: A Case Study on Retraining Variants","date":"2021-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lehduong/NPTM","path":"hrank/models/resnet_imagenet.py","file_url":"https://github.com/lehduong/NPTM/blob/HEAD/hrank/models/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.03193","paper":"/paper/network-pruning-that-matters-a-case-study-on-1","title":"Network Pruning That Matters: A Case Study on Retraining Variants","date":"2021-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lehduong/NPTM","path":"hrank/models/resnet_cifar.py","file_url":"https://github.com/lehduong/NPTM/blob/HEAD/hrank/models/resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2105.02467","paper":"/paper/body-meshes-as-points","title":"Body Meshes as Points","date":"2021-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jfzhang95/BMP","path":"mmcv/mmcv/cnn/resnet.py","file_url":"https://github.com/jfzhang95/BMP/blob/HEAD/mmcv/mmcv/cnn/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"77f89e05c55c985d","mcp_get_code":{"code_sha256":"77f89e05c55c985d"}},{"arxiv_id":"2105.01289","paper":"/paper/representation-learning-for-clustering-via","title":"Representation Learning for Clustering via Building Consensus","date":"2021-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JayanthRR/ConCURL_NCE","path":"model_utils/nce_resnet.py","file_url":"https://github.com/JayanthRR/ConCURL_NCE/blob/HEAD/model_utils/nce_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2105.00636","paper":"/paper/learning-to-drive-from-a-world-on-rails","title":"Learning to drive from a world on rails","date":"2021-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dotchen/WorldOnRails","path":"common/resnet.py","file_url":"https://github.com/dotchen/WorldOnRails/blob/HEAD/common/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2104.14222","paper":"/paper/privacy-preserving-portrait-matting","title":"Privacy-Preserving Portrait Matting","date":"2021-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JizhiziLi/P3M","path":"core/network/resnet_mp.py","file_url":"https://github.com/JizhiziLi/P3M/blob/HEAD/core/network/resnet_mp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.14222","paper":"/paper/privacy-preserving-portrait-matting","title":"Privacy-Preserving Portrait Matting","date":"2021-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JizhiziLi/P3M","path":"core/network/P3mNet.py","file_url":"https://github.com/JizhiziLi/P3M/blob/HEAD/core/network/P3mNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.13766","paper":"/paper/boosting-co-teaching-with-compression","title":"Boosting Co-teaching with Compression Regularization for Label Noise","date":"2021-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yingyichen-cyy/Nested-Co-teaching","path":"co_teaching_resnet/model/imagenet_resnet.py","file_url":"https://github.com/yingyichen-cyy/Nested-Co-teaching/blob/HEAD/co_teaching_resnet/model/imagenet_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.12357","paper":"/paper/vcgan-video-colorization-with-hybrid","title":"VCGAN: Video Colorization with Hybrid Generative Adversarial Network","date":"2021-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaoyuzhi/VCGAN","path":"resnet50_in_pre_training/network.py","file_url":"https://github.com/zhaoyuzhi/VCGAN/blob/HEAD/resnet50_in_pre_training/network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.11207","paper":"/paper/fully-convolutional-line-parsing","title":"Fully Convolutional Line Parsing","date":"2021-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Delay-Xili/F-Clip","path":"FClip/models/pose_hrnet.py","file_url":"https://github.com/Delay-Xili/F-Clip/blob/HEAD/FClip/models/pose_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.09937","paper":"/paper/gradient-matching-for-domain-generalization","title":"Gradient Matching for Domain Generalization","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YugeTen/fish","path":"src/models/resnet_multispectral.py","file_url":"https://github.com/YugeTen/fish/blob/HEAD/src/models/resnet_multispectral.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.09841","paper":"/paper/selfreg-self-supervised-contrastive","title":"SelfReg: Self-supervised Contrastive Regularization for Domain Generalization","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dnap512/SelfReg","path":"codes/model/resnet18_selfreg.py","file_url":"https://github.com/dnap512/SelfReg/blob/HEAD/codes/model/resnet18_selfreg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.08215","paper":"/paper/bnn-bn-training-binary-neural-networks","title":"\"BNN - BN = ?\": Training Binary Neural Networks without Batch Normalization","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/BNN_NoBN","path":"models/Qa_reactnet_18_bn.py","file_url":"https://github.com/VITA-Group/BNN_NoBN/blob/HEAD/models/Qa_reactnet_18_bn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.08215","paper":"/paper/bnn-bn-training-binary-neural-networks","title":"\"BNN - BN = ?\": Training Binary Neural Networks without Batch Normalization","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/BNN_NoBN","path":"models/Qa_reactnet_18_bf.py","file_url":"https://github.com/VITA-Group/BNN_NoBN/blob/HEAD/models/Qa_reactnet_18_bf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"20f90730b09ba843","mcp_get_code":{"code_sha256":"20f90730b09ba843"}},{"arxiv_id":"2104.07986","paper":"/paper/learning-to-reconstruct-3d-non-cuboid-room","title":"Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CYang0515/NonCuboidRoom","path":"models/hrnet.py","file_url":"https://github.com/CYang0515/NonCuboidRoom/blob/HEAD/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.07446","paper":"/paper/rehearsal-revealed-the-limits-and-merits-of","title":"Rehearsal revealed: The limits and merits of revisiting samples in continual learning","date":"2021-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mattdl/RehearsalRevealed","path":"ridge_aversion_exp/model.py","file_url":"https://github.com/Mattdl/RehearsalRevealed/blob/HEAD/ridge_aversion_exp/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2104.06770","paper":"/paper/graph-based-person-signature-for-person-re","title":"Graph-based Person Signature for Person Re-Identifications","date":"2021-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aioz-ai/CVPRW21_GPS","path":"modeling/backbones/resnet.py","file_url":"https://github.com/aioz-ai/CVPRW21_GPS/blob/HEAD/modeling/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2104.06159","paper":"/paper/muesli-combining-improvements-in-policy","title":"Muesli: Combining Improvements in Policy Optimization","date":"2021-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Itomigna2/Muesli-lunarlander","path":"Muesli_lunar_rgb.py","file_url":"https://github.com/Itomigna2/Muesli-lunarlander/blob/HEAD/Muesli_lunar_rgb.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dad29e46e1d9e83","mcp_get_code":{"code_sha256":"2dad29e46e1d9e83"}},{"arxiv_id":"2104.05170","paper":"/paper/memory-guided-unsupervised-image-to-image","title":"Memory-guided Unsupervised Image-to-image Translation","date":"2021-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HsinYingLee/DRIT","path":"src/model.py","file_url":"https://github.com/HsinYingLee/DRIT/blob/HEAD/src/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8b7ae4531a13031","mcp_get_code":{"code_sha256":"c8b7ae4531a13031"}},{"arxiv_id":"2104.03736","paper":"/paper/support-target-protocol-for-meta-learning","title":"Towards Enabling Meta-Learning from Target Models","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"njulus/ST","path":"networks/resnet.py","file_url":"https://github.com/njulus/ST/blob/HEAD/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2104.03501","paper":"/paper/deepi2p-image-to-point-cloud-registration-via","title":"DeepI2P: Image-to-Point Cloud Registration via Deep Classification","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijx10/DeepI2P","path":"models/resnet.py","file_url":"https://github.com/lijx10/DeepI2P/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.03047","paper":"/paper/few-shot-incremental-learning-with","title":"Few-Shot Incremental Learning with Continually Evolved Classifiers","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icoz69/cec-cvpr2021","path":"models/resnet18_encoder.py","file_url":"https://github.com/icoz69/cec-cvpr2021/blob/HEAD/models/resnet18_encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.03047","paper":"/paper/few-shot-incremental-learning-with","title":"Few-Shot Incremental Learning with Continually Evolved Classifiers","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icoz69/cec-cvpr2021","path":"models/resnet20_cifar.py","file_url":"https://github.com/icoz69/cec-cvpr2021/blob/HEAD/models/resnet20_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.02244","paper":"/paper/content-aware-gan-compression","title":"Content-Aware GAN Compression","date":"2021-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lychenyoko/content-aware-gan-compression","path":"Util/content_aware_pruning.py","file_url":"https://github.com/lychenyoko/content-aware-gan-compression/blob/HEAD/Util/content_aware_pruning.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2104.02226","paper":"/paper/beyond-categorical-label-representations-for-1","title":"Beyond Categorical Label Representations for Image Classification","date":"2021-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BoyuanChen/label_representations","path":"models/cifar/preresnet.py","file_url":"https://github.com/BoyuanChen/label_representations/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.01320","paper":"/paper/an-empirical-study-on-channel-effects-for","title":"An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems","date":"2021-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yzyouzhang/AIR-ASVspoof","path":"resnet.py","file_url":"https://github.com/yzyouzhang/AIR-ASVspoof/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2104.01231","paper":"/paper/misclassification-aware-gaussian-smoothing","title":"Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration","date":"2021-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TheoT1/DiGN","path":"imagenet_models/resnet.py","file_url":"https://github.com/TheoT1/DiGN/blob/HEAD/imagenet_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.01231","paper":"/paper/misclassification-aware-gaussian-smoothing","title":"Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration","date":"2021-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TheoT1/DiGN","path":"imagenet_models/leaky_resnet.py","file_url":"https://github.com/TheoT1/DiGN/blob/HEAD/imagenet_models/leaky_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.00749","paper":"/paper/confidence-adaptive-anytime-pixel-level","title":"Anytime Dense Prediction with Confidence Adaptivity","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuzhuang13/anytime","path":"lib/models/model_anytime.py","file_url":"https://github.com/liuzhuang13/anytime/blob/HEAD/lib/models/model_anytime.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adcb637c87d2d6fd","mcp_get_code":{"code_sha256":"adcb637c87d2d6fd"}},{"arxiv_id":"2104.00680","paper":"/paper/loftr-detector-free-local-feature-matching","title":"LoFTR: Detector-Free Local Feature Matching with Transformers","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zju3dv/LoFTR","path":"src/loftr/backbone/resnet_fpn.py","file_url":"https://github.com/zju3dv/LoFTR/blob/HEAD/src/loftr/backbone/resnet_fpn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.00466","paper":"/paper/improving-calibration-for-long-tailed-1","title":"Improving Calibration for Long-Tailed Recognition","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jia-Research-Lab/MiSLAS","path":"models/resnet.py","file_url":"https://github.com/Jia-Research-Lab/MiSLAS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.00405","paper":"/paper/avalanche-an-end-to-end-library-for-continual","title":"Avalanche: an End-to-End Library for Continual Learning","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lrzpellegrini/avalanche","path":"avalanche/models/icarl_resnet.py","file_url":"https://github.com/lrzpellegrini/avalanche/blob/HEAD/avalanche/models/icarl_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f522c857bf857a12","mcp_get_code":{"code_sha256":"f522c857bf857a12"}},{"arxiv_id":"2104.00233","paper":"/paper/unsupervised-domain-expansion-for-visual","title":"Unsupervised Domain Expansion for Visual Categorization","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theeighthday/co-teaching","path":"model/ResNet.py","file_url":"https://github.com/theeighthday/co-teaching/blob/HEAD/model/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.00233","paper":"/paper/unsupervised-domain-expansion-for-visual","title":"Unsupervised Domain Expansion for Visual Categorization","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theeighthday/co-teaching","path":"model/SRDC.py","file_url":"https://github.com/theeighthday/co-teaching/blob/HEAD/model/SRDC.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2104.00170","paper":"/paper/an-investigation-of-critical-issues-in-bias","title":"Are Bias Mitigation Techniques for Deep Learning Effective?","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"erobic/bias-mitigators","path":"models/variable_width_resnet.py","file_url":"https://github.com/erobic/bias-mitigators/blob/HEAD/models/variable_width_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2104.02610","paper":"/paper/on-the-robustness-of-vision-transformers-to","title":"On the Robustness of Vision Transformers to Adversarial Examples","date":"2021-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MetaMain/ViTRobust","path":"VisionTransformersRobustness/VisionTransformersRobustness/TransformerResNet.py","file_url":"https://github.com/MetaMain/ViTRobust/blob/HEAD/VisionTransformersRobustness/VisionTransformersRobustness/TransformerResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2104.02610","paper":"/paper/on-the-robustness-of-vision-transformers-to","title":"On the Robustness of Vision Transformers to Adversarial Examples","date":"2021-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MetaMain/ViTRobust","path":"VisionTransformersRobustness/VisionTransformersRobustness/BigTransferModels.py","file_url":"https://github.com/MetaMain/ViTRobust/blob/HEAD/VisionTransformersRobustness/VisionTransformersRobustness/BigTransferModels.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"2103.17268","paper":"/paper/fast-certified-robust-training-via-better","title":"Fast Certified Robust Training with Short Warmup","date":"2021-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shizhouxing/Fast-Certified-Robust-Training","path":"models/wide_resnet.py","file_url":"https://github.com/shizhouxing/Fast-Certified-Robust-Training/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2103.16470","paper":"/paper/depth-conditioned-dynamic-message-propagation","title":"Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-zvg/DDMP","path":"models/resnet.py","file_url":"https://github.com/fudan-zvg/DDMP/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2103.16257","paper":"/paper/model-contrastive-federated-learning","title":"Model-Contrastive Federated Learning","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"QinbinLi/MOON","path":"resnetcifar.py","file_url":"https://github.com/QinbinLi/MOON/blob/HEAD/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2103.16083","paper":"/paper/fully-convolutional-scene-graph-generation","title":"Fully Convolutional Scene Graph Generation","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuhengyue/fcsgg","path":"fcsgg/modeling/backbone/dla.py","file_url":"https://github.com/liuhengyue/fcsgg/blob/HEAD/fcsgg/modeling/backbone/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2103.15375","paper":"/paper/alignmix-improving-representation-by","title":"AlignMixup: Improving Representations By Interpolating Aligned Features","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shashankvkt/alignmixup_cvpr22","path":"imagenet/models/resnet.py","file_url":"https://github.com/shashankvkt/alignmixup_cvpr22/blob/HEAD/imagenet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2103.15375","paper":"/paper/alignmix-improving-representation-by","title":"AlignMixup: Improving Representations By Interpolating Aligned Features","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shashankvkt/alignmixup_cvpr22","path":"cifar10_100/models/WideResnet.py","file_url":"https://github.com/shashankvkt/alignmixup_cvpr22/blob/HEAD/cifar10_100/models/WideResnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2103.15375","paper":"/paper/alignmix-improving-representation-by","title":"AlignMixup: Improving Representations By Interpolating Aligned Features","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shashankvkt/alignmixup_cvpr22","path":"cifar10_100/models/resnet.py","file_url":"https://github.com/shashankvkt/alignmixup_cvpr22/blob/HEAD/cifar10_100/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abman23/pmnet","path":"models/vgg16.py","file_url":"https://github.com/abman23/pmnet/blob/HEAD/models/vgg16.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbbe1940f6850b5c","mcp_get_code":{"code_sha256":"dbbe1940f6850b5c"}},{"arxiv_id":"2103.14023","paper":"/paper/agentformer-agent-aware-transformers-for","title":"AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Khrylx/AgentFormer","path":"model/common/resnet.py","file_url":"https://github.com/Khrylx/AgentFormer/blob/HEAD/model/common/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2103.13859","paper":"/paper/group-cam-group-score-weighted-visual","title":"Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wofmanaf/Group-CAM","path":"backbones/resnet.py","file_url":"https://github.com/wofmanaf/Group-CAM/blob/HEAD/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2103.13253","paper":"/paper/learning-versatile-neural-architectures-by","title":"Learning Versatile Neural Architectures by Propagating Network Codes","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/NCP","path":"models/supernet.py","file_url":"https://github.com/dingmyu/NCP/blob/HEAD/models/supernet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"52b42ce8214983fb","mcp_get_code":{"code_sha256":"52b42ce8214983fb"}},{"arxiv_id":"2103.13023","paper":"/paper/can-vision-transformers-learn-without-natural","title":"Can Vision Transformers Learn without Natural Images?","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch","path":"finetuning/resnet.py","file_url":"https://github.com/hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch/blob/HEAD/finetuning/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2103.12710","paper":"/paper/spatial-intention-maps-for-multi-agent-mobile","title":"Spatial Intention Maps for Multi-Agent Mobile Manipulation","date":"2021-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimmyyhwu/spatial-intention-maps","path":"resnet.py","file_url":"https://github.com/jimmyyhwu/spatial-intention-maps/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2103.11568","paper":"/paper/cluster-contrast-for-unsupervised-person-re","title":"Cluster Contrast for Unsupervised Person Re-Identification","date":"2021-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/cluster-contrast","path":"clustercontrast/models/resnet_ibn_a.py","file_url":"https://github.com/alibaba/cluster-contrast/blob/HEAD/clustercontrast/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2103.08756","paper":"/paper/revisiting-dynamic-convolution-via-matrix-1","title":"Revisiting Dynamic Convolution via Matrix Decomposition","date":"2021-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyunsheng13/dcd","path":"models/imagenet/resnet_dcd.py","file_url":"https://github.com/liyunsheng13/dcd/blob/HEAD/models/imagenet/resnet_dcd.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"138525ef5b7a7291","mcp_get_code":{"code_sha256":"138525ef5b7a7291"}},{"arxiv_id":"2103.07254","paper":"/paper/deep-dual-consecutive-network-for-human-pose","title":"Deep Dual Consecutive Network for Human Pose Estimation","date":"2021-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pose-Group/DCPose","path":"posetimation/zoo/DcPose/dcpose_rsn.py","file_url":"https://github.com/Pose-Group/DCPose/blob/HEAD/posetimation/zoo/DcPose/dcpose_rsn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e9bddedbc350840c","mcp_get_code":{"code_sha256":"e9bddedbc350840c"}},{"arxiv_id":"2102.12677","paper":"/paper/do-not-let-privacy-overbill-utility-gradient-1","title":"Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning","date":"2021-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dayu11/Gradient-Embedding-Perturbation","path":"models/resnet_cifar.py","file_url":"https://github.com/dayu11/Gradient-Embedding-Perturbation/blob/HEAD/models/resnet_cifar.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"864390d230441893","mcp_get_code":{"code_sha256":"864390d230441893"}},{"arxiv_id":"2102.11855","paper":"/paper/deep-convolutional-neural-networks-with-1","title":"Deep Unitary Convolutional Neural Networks","date":"2021-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"h-chang/uresnet","path":"model/model.py","file_url":"https://github.com/h-chang/uresnet/blob/HEAD/model/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4cd86daf8278d919","mcp_get_code":{"code_sha256":"4cd86daf8278d919"}},{"arxiv_id":"2102.09896","paper":"/paper/scribble-supervised-semantic-segmentation-by-1","title":"Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace","date":"2021-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"panzhiyi/URSS","path":"model_RW.py","file_url":"https://github.com/panzhiyi/URSS/blob/HEAD/model_RW.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89cb859ec28c5819","mcp_get_code":{"code_sha256":"89cb859ec28c5819"}},{"arxiv_id":"2102.08602","paper":"/paper/lambdanetworks-modeling-long-range-1","title":"LambdaNetworks: Modeling Long-Range Interactions Without Attention","date":"2021-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2102.05426","paper":"/paper/brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2102.04306","paper":"/paper/transunet-transformers-make-strong-encoders","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","date":"2021-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maloadba/mgenseg_2d","path":"model/configs/mbrats/Transunet.py","file_url":"https://github.com/maloadba/mgenseg_2d/blob/HEAD/model/configs/mbrats/Transunet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e5bf13dbdc4f008","mcp_get_code":{"code_sha256":"4e5bf13dbdc4f008"}},{"arxiv_id":"2102.00240","paper":"/paper/sa-net-shuffle-attention-for-deep","title":"SA-Net: Shuffle Attention for Deep Convolutional Neural Networks","date":"2021-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2011.03308","paper":"/paper/towards-efficient-scene-understanding-via","title":"Towards Efficient Scene Understanding via Squeeze Reasoning","date":"2020-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2011.00844","paper":"/paper/do-2d-gans-know-3d-shape-unsupervised-3d-1","title":"Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs","date":"2020-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XingangPan/GAN2Shape","path":"gan2shape/parsing/resnet.py","file_url":"https://github.com/XingangPan/GAN2Shape/blob/HEAD/gan2shape/parsing/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.16188","paper":"/paper/end-to-end-animal-image-matting","title":"Bridging Composite and Real: Towards End-to-end Deep Image Matting","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.15440","paper":"/paper/flatnet-towards-photorealistic-scene","title":"FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siddiquesalman/flatnet","path":"models.py","file_url":"https://github.com/siddiquesalman/flatnet/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2010.14535","paper":"/paper/neural-architecture-search-of-spd-manifold-1","title":"Neural Architecture Search of SPD Manifold Networks","date":"2020-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rheasukthanker/spdnetnas","path":"Code/Model.py","file_url":"https://github.com/rheasukthanker/spdnetnas/blob/HEAD/Code/Model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.12136","paper":"/paper/lightweight-generative-adversarial-networks","title":"Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation","date":"2020-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"2010.11757","paper":"/paper/deep-analysis-of-cnn-based-spatio-temporal","title":"Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/action-recognition-pytorch","path":"models/twod_models/resnet.py","file_url":"https://github.com/IBM/action-recognition-pytorch/blob/HEAD/models/twod_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.11742","paper":"/paper/learning-black-box-attackers-with","title":"Learning Black-Box Attackers with Transferable Priors and Query Feedback","date":"2020-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TrustworthyDL/LeBA","path":"imagenet/models/resnet.py","file_url":"https://github.com/TrustworthyDL/LeBA/blob/HEAD/imagenet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2010.09151","paper":"/paper/learnable-spectro-temporal-receptive-fields","title":"Learnable Spectro-temporal Receptive Fields for Robust Voice Type Discrimination","date":null,"month_inferred_from_arxiv_id":"2020-10","title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"2010.09080","paper":"/paper/poisoned-classifiers-are-not-only-backdoored-1","title":"Poisoned classifiers are not only backdoored, they are fundamentally broken","date":"2020-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/breaking-poisoned-classifier","path":"code/badnet/alexnet_fc7out.py","file_url":"https://github.com/locuslab/breaking-poisoned-classifier/blob/HEAD/code/badnet/alexnet_fc7out.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2010.08657","paper":"/paper/class-incremental-learning-with-pre-allocated","title":"Class-incremental Learning with Pre-allocated Fixed Classifiers","date":"2020-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2010.07092","paper":"/paper/data-augmentation-for-meta-learning-1","title":"Data Augmentation for Meta-Learning","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RenkunNi/MetaAug","path":"models/ResNet12_embedding.py","file_url":"https://github.com/RenkunNi/MetaAug/blob/HEAD/models/ResNet12_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.06897","paper":"/paper/adaptive-attentive-geolocalization-from-few","title":"Adaptive-Attentive Geolocalization from few queries: a hybrid approach","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeriopaolicelli/AdAGeo","path":"src/resnet.py","file_url":"https://github.com/valeriopaolicelli/AdAGeo/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.05595","paper":"/paper/rethinking-experience-replay-a-bag-of-tricks","title":"Rethinking Experience Replay: a Bag of Tricks for Continual Learning","date":"2020-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hastings24/rethinking_er","path":"backbone/ResNet.py","file_url":"https://github.com/hastings24/rethinking_er/blob/HEAD/backbone/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"2010.05063","paper":"/paper/meta-aggregating-networks-for-class-1","title":"Adaptive Aggregation Networks for Class-Incremental Learning","date":"2020-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/class-incremental-learning","path":"adaptive-aggregation-networks/models/modified_resnet_cifar.py","file_url":"https://github.com/yaoyao-liu/class-incremental-learning/blob/HEAD/adaptive-aggregation-networks/models/modified_resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2010.05063","paper":"/paper/meta-aggregating-networks-for-class-1","title":"Adaptive Aggregation Networks for Class-Incremental Learning","date":"2020-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/class-incremental-learning","path":"adaptive-aggregation-networks/models/modified_resnet.py","file_url":"https://github.com/yaoyao-liu/class-incremental-learning/blob/HEAD/adaptive-aggregation-networks/models/modified_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2010.03558","paper":"/paper/high-capacity-expert-binary-networks-1","title":"High-Capacity Expert Binary Networks","date":"2020-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1adrianb/expert-binary-networks","path":"models/eb_resnet.py","file_url":"https://github.com/1adrianb/expert-binary-networks/blob/HEAD/models/eb_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85ada31d24c24b8c","mcp_get_code":{"code_sha256":"85ada31d24c24b8c"}},{"arxiv_id":"2010.02637","paper":"/paper/disentangled-generative-causal-representation-1","title":"Weakly Supervised Disentangled Generative Causal Representation Learning","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xwshen51/DEAR","path":"resnet.py","file_url":"https://github.com/xwshen51/DEAR/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2010.01051","paper":"/paper/neural-bootstrapper","title":"Neural Bootstrapper","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungbinlim/GBS","path":"models/wideresnet.py","file_url":"https://github.com/sungbinlim/GBS/blob/HEAD/models/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2010.01051","paper":"/paper/neural-bootstrapper","title":"Neural Bootstrapper","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungbinlim/GBS","path":"models/resnet.py","file_url":"https://github.com/sungbinlim/GBS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2010.01051","paper":"/paper/neural-bootstrapper","title":"Neural Bootstrapper","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungbinlim/GBS","path":"models/resnet_ca.py","file_url":"https://github.com/sungbinlim/GBS/blob/HEAD/models/resnet_ca.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2010.00763","paper":"/paper/bongard-logo-a-new-benchmark-for-human-level","title":"Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/Bongard-LOGO","path":"Bongard-LOGO_Baselines/models/resnet.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2010.00763","paper":"/paper/bongard-logo-a-new-benchmark-for-human-level","title":"Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/Bongard-LOGO","path":"Bongard-LOGO_Baselines/models/resnet12.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/resnet12.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"2009.14119","paper":"/paper/asymmetric-loss-for-multi-label","title":"Asymmetric Loss For Multi-Label Classification","date":"2020-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mrT23/TResNet","path":"src/models/tresnet_v2/tresnet_v2.py","file_url":"https://github.com/mrT23/TResNet/blob/HEAD/src/models/tresnet_v2/tresnet_v2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2009.14082","paper":"/paper/attentional-feature-fusion","title":"Attentional Feature Fusion","date":"2020-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YimianDai/open-aff","path":"aff_pytorch/aff_net/aff_resnet.py","file_url":"https://github.com/YimianDai/open-aff/blob/HEAD/aff_pytorch/aff_net/aff_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2009.13333","paper":"/paper/group-whitening-balancing-learning-efficiency","title":"Group Whitening: Balancing Learning Efficiency and Representational Capacity","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/GroupWhitening","path":"classification/ImageNet/models/resnext.py","file_url":"https://github.com/huangleiBuaa/GroupWhitening/blob/HEAD/classification/ImageNet/models/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2009.13333","paper":"/paper/group-whitening-balancing-learning-efficiency","title":"Group Whitening: Balancing Learning Efficiency and Representational Capacity","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/GroupWhitening","path":"classification/ImageNet/models/resnet.py","file_url":"https://github.com/huangleiBuaa/GroupWhitening/blob/HEAD/classification/ImageNet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2009.10013","paper":"/paper/synthetic-training-for-accurate-3d-human-pose","title":"Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akashsengupta1997/STRAPS-3DHumanShapePose","path":"models/resnet.py","file_url":"https://github.com/akashsengupta1997/STRAPS-3DHumanShapePose/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2009.09960","paper":"/paper/towards-fast-accurate-and-stable-3d-dense-1","title":"Towards Fast, Accurate and Stable 3D Dense Face Alignment","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleardusk/3DDFA_V2","path":"models/resnet.py","file_url":"https://github.com/cleardusk/3DDFA_V2/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2009.09405","paper":"/paper/scale-localized-abstract-reasoning","title":"Scale-Localized Abstract Reasoning","date":"2020-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanivbenny/MRNet","path":"src/networks/blocks.py","file_url":"https://github.com/yanivbenny/MRNet/blob/HEAD/src/networks/blocks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"52601677ce5d1634","mcp_get_code":{"code_sha256":"52601677ce5d1634"}},{"arxiv_id":"2009.09405","paper":"/paper/scale-localized-abstract-reasoning","title":"Scale-Localized Abstract Reasoning","date":"2020-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanivbenny/MRNet","path":"src/networks/mrnet.py","file_url":"https://github.com/yanivbenny/MRNet/blob/HEAD/src/networks/mrnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"11fda3e1d6f755ae","mcp_get_code":{"code_sha256":"11fda3e1d6f755ae"}},{"arxiv_id":"2009.07378","paper":"/paper/bop-challenge-2020-on-6d-object-localization","title":"BOP Challenge 2020 on 6D Object Localization","date":"2020-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azad96/cosypose-custom","path":"cosypose/models/wide_resnet.py","file_url":"https://github.com/azad96/cosypose-custom/blob/HEAD/cosypose/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2009.04960","paper":"/paper/prototype-completion-with-primitive-knowledge","title":"Prototype Completion with Primitive Knowledge for Few-Shot Learning","date":"2020-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangbq-research/Prototype_Completion_for_FSL","path":"models/resnet12_2.py","file_url":"https://github.com/zhangbq-research/Prototype_Completion_for_FSL/blob/HEAD/models/resnet12_2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"2009.04809","paper":"/paper/deep-iterative-residual-convolutional-network","title":"Deep Iterative Residual Convolutional Network for Single Image Super-Resolution","date":"2020-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RaoUmer/ISRResCNet","path":"isrrescnet_code_demo/models/ISRResCNet.py","file_url":"https://github.com/RaoUmer/ISRResCNet/blob/HEAD/isrrescnet_code_demo/models/ISRResCNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cebcc41768bc1cd4","mcp_get_code":{"code_sha256":"cebcc41768bc1cd4"}},{"arxiv_id":"2009.03693","paper":"/paper/deep-cyclic-generative-adversarial-residual","title":"Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution","date":"2020-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RaoUmer/SRResCycGAN","path":"srrescycgan_code_demo/models/ResDNet.py","file_url":"https://github.com/RaoUmer/SRResCycGAN/blob/HEAD/srrescycgan_code_demo/models/ResDNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f80f003de17602a","mcp_get_code":{"code_sha256":"1f80f003de17602a"}},{"arxiv_id":"2009.03632","paper":"/paper/imbalanced-continual-learning-with","title":"Imbalanced Continual Learning with Partitioning Reservoir Sampling","date":"2020-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2009.09805","paper":"/paper/learning-audio-visual-representations-with","title":"Active Contrastive Learning of Audio-Visual Video Representations","date":"2020-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunyikristy/CM-ACC","path":"resnet1d.py","file_url":"https://github.com/yunyikristy/CM-ACC/blob/HEAD/resnet1d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c68e0b906dbc9ef","mcp_get_code":{"code_sha256":"3c68e0b906dbc9ef"}},{"arxiv_id":"2008.07043","paper":"/paper/oriented-object-detection-in-aerial-images","title":"Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors","date":"2020-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yijingru/BBAVectors-Oriented-Object-Detection","path":"models/resnet.py","file_url":"https://github.com/yijingru/BBAVectors-Oriented-Object-Detection/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2008.06963","paper":"/paper/do-not-disturb-me-person-re-identification","title":"Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians","date":"2020-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"X-BrainLab/PI-ReID","path":"modeling/backbones/pisnet.py","file_url":"https://github.com/X-BrainLab/PI-ReID/blob/HEAD/modeling/backbones/pisnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2008.06133","paper":"/paper/3d-bird-reconstruction-a-dataset-model-and","title":"3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marcbadger/avian-mesh","path":"keypoint_detection/pose_hrnet.py","file_url":"https://github.com/marcbadger/avian-mesh/blob/HEAD/keypoint_detection/pose_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2008.05975","paper":"/paper/deep-learning-to-quantify-pulmonary-edema-in","title":"Deep Learning to Quantify Pulmonary Edema in Chest Radiographs","date":"2020-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RayRuizhiLiao/resnet_chestxray","path":"resnet_chestxray/model.py","file_url":"https://github.com/RayRuizhiLiao/resnet_chestxray/blob/HEAD/resnet_chestxray/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2008.04115","paper":"/paper/t-gd-transferable-gan-generated-images","title":"T-GD: Transferable GAN-generated Images Detection Framework","date":"2020-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cutz-j/T-GD","path":"resnext/model.py","file_url":"https://github.com/cutz-j/T-GD/blob/HEAD/resnext/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83b10a4972562225","mcp_get_code":{"code_sha256":"83b10a4972562225"}},{"arxiv_id":"2008.03813","paper":"/paper/unsupervised-feature-learning-by-cross-level","title":"Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination","date":"2020-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frank-xwang/CLD-UnsupervisedLearning","path":"infomin/resnet.py","file_url":"https://github.com/frank-xwang/CLD-UnsupervisedLearning/blob/HEAD/infomin/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2008.01352","paper":"/paper/pde-driven-spatiotemporal-disentanglement","title":"PDE-Driven Spatiotemporal Disentanglement","date":"2020-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JeremDona/spatiotemporal_variable_separation","path":"var_sep/networks/conv.py","file_url":"https://github.com/JeremDona/spatiotemporal_variable_separation/blob/HEAD/var_sep/networks/conv.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7599edadbb4f2bdc","mcp_get_code":{"code_sha256":"7599edadbb4f2bdc"}},{"arxiv_id":"2008.00975","paper":"/paper/seco-exploring-sequence-supervision-for","title":"SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning","date":"2020-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YihengZhang-CV/SeCo-Sequence-Contrastive-Learning","path":"seco/resnet_mlp.py","file_url":"https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning/blob/HEAD/seco/resnet_mlp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2008.00823","paper":"/paper/rethinking-image-deraining-via-rain-streaks","title":"Rethinking Image Deraining via Rain Streaks and Vapors","date":"2020-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yluestc/derain","path":"models/Shuffle_Transfer_Net.py","file_url":"https://github.com/yluestc/derain/blob/HEAD/models/Shuffle_Transfer_Net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30b9fcc9a93dfd43","mcp_get_code":{"code_sha256":"30b9fcc9a93dfd43"}},{"arxiv_id":"2007.14902","paper":"/paper/linear-attention-mechanism-an-efficient","title":"Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation","date":"2020-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2007.13890","paper":"/paper/unsupervised-domain-adaptation-in-the","title":"Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification","date":"2020-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"djidje/D-MMD","path":"torchreid/models/resnet.py","file_url":"https://github.com/djidje/D-MMD/blob/HEAD/torchreid/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"2007.13870","paper":"/paper/a-unified-framework-of-surrogate-loss-by","title":"A Unified Framework of Surrogate Loss by Refactoring and Interpolation","date":"2020-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/uniloss","path":"multi_classification/resnet.py","file_url":"https://github.com/princeton-vl/uniloss/blob/HEAD/multi_classification/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2007.12668","paper":"/paper/kprnet-improving-projection-based-lidar","title":"KPRNet: Improving projection-based LiDAR semantic segmentation","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeyvidKochanov-TomTom/kprnet","path":"models/resnet.py","file_url":"https://github.com/DeyvidKochanov-TomTom/kprnet/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2007.12107","paper":"/paper/few-shot-object-detection-and-viewpoint","title":"Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild","date":"2020-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YoungXIAO13/PoseContrast","path":"src/model/resnet.py","file_url":"https://github.com/YoungXIAO13/PoseContrast/blob/HEAD/src/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.10587","paper":"/paper/learning-to-compose-hypercolumns-for-visual","title":"Learning to Compose Hypercolumns for Visual Correspondence","date":"2020-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juhongm999/dhpf","path":"model/base/resnet.py","file_url":"https://github.com/juhongm999/dhpf/blob/HEAD/model/base/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a0747d1b610d85f0","mcp_get_code":{"code_sha256":"a0747d1b610d85f0"}},{"arxiv_id":"2007.10538","paper":"/paper/regularizing-deep-networks-with-semantic-data","title":"Regularizing Deep Networks with Semantic Data Augmentation","date":"2020-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"speedinghzl/pytorch-segmentation-toolbox","path":"networks/deeplabv3.py","file_url":"https://github.com/speedinghzl/pytorch-segmentation-toolbox/blob/HEAD/networks/deeplabv3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.09933","paper":"/paper/motionsqueeze-neural-motion-feature-learning","title":"MotionSqueeze: Neural Motion Feature Learning for Video Understanding","date":"2020-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arunos728/MotionSqueeze","path":"resnet_TSM.py","file_url":"https://github.com/arunos728/MotionSqueeze/blob/HEAD/resnet_TSM.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.09548","paper":"/paper/kinematic-3d-object-detection-in-monocular","title":"Kinematic 3D Object Detection in Monocular Video","date":"2020-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nicholasli1995/EgoNet","path":"libs/model/heatmapModel/hrnet.py","file_url":"https://github.com/Nicholasli1995/EgoNet/blob/HEAD/libs/model/heatmapModel/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.09365","paper":"/paper/malleable-2-5d-convolution-learning-receptive","title":"Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing","date":"2020-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"charlesCXK/RGBD_Semantic_Segmentation_PyTorch","path":"furnace/base_model/resnet.py","file_url":"https://github.com/charlesCXK/RGBD_Semantic_Segmentation_PyTorch/blob/HEAD/furnace/base_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.09271","paper":"/paper/onlineaugment-online-data-augmentation-with","title":"OnlineAugment: Online Data Augmentation with Less Domain Knowledge","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiqiangdon/online-augment","path":"models/pyramidnet.py","file_url":"https://github.com/zhiqiangdon/online-augment/blob/HEAD/models/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"2007.09070","paper":"/paper/hybrid-discriminative-generative-training-via","title":"Hybrid Discriminative-Generative Training via Contrastive Learning","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lhao499/HDGE","path":"wideresnet.py","file_url":"https://github.com/lhao499/HDGE/blob/HEAD/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2007.09062","paper":"/paper/multi-scale-interactive-network-for-salient-1","title":"Multi-scale Interactive Network for Salient Object Detection","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lartpang/MINet","path":"code/backbone/origin/resnet.py","file_url":"https://github.com/lartpang/MINet/blob/HEAD/code/backbone/origin/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.08856","paper":"/paper/epnet-enhancing-point-features-with-image","title":"EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"happinesslz/EPNet","path":"lib/net/pointnet2_msg.py","file_url":"https://github.com/happinesslz/EPNet/blob/HEAD/lib/net/pointnet2_msg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.08454","paper":"/paper/shape-prior-deformation-for-categorical-6d","title":"Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation","date":"2020-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gritYCDA/shape_deform","path":"lib/pspnet.py","file_url":"https://github.com/gritYCDA/shape_deform/blob/HEAD/lib/pspnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3fc43aa1154384a","mcp_get_code":{"code_sha256":"a3fc43aa1154384a"}},{"arxiv_id":"2007.07936","paper":"/paper/classmix-segmentation-based-data-augmentation","title":"ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WilhelmT/ClassMix","path":"model/deeplabv2.py","file_url":"https://github.com/WilhelmT/ClassMix/blob/HEAD/model/deeplabv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.07695","paper":"/paper/label-propagation-with-augmented-anchors-a","title":"Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YBZh/Label-Propagation-with-Augmented-Anchors","path":"models/resnet.py","file_url":"https://github.com/YBZh/Label-Propagation-with-Augmented-Anchors/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.07435","paper":"/paper/advflow-inconspicuous-black-box-adversarial","title":"AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmdolatabadi/AdvFlow","path":"wide_resnets.py","file_url":"https://github.com/hmdolatabadi/AdvFlow/blob/HEAD/wide_resnets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2007.07435","paper":"/paper/advflow-inconspicuous-black-box-adversarial","title":"AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmdolatabadi/AdvFlow","path":"resnet.py","file_url":"https://github.com/hmdolatabadi/AdvFlow/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2007.06227","paper":"/paper/hierarchical-dynamic-filtering-network-for","title":"Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection","date":"2020-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lartpang/HDFNet","path":"backbone/ResNet.py","file_url":"https://github.com/lartpang/HDFNet/blob/HEAD/backbone/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.04269","paper":"/paper/segfix-model-agnostic-boundary-refinement-for","title":"SegFix: Model-Agnostic Boundary Refinement for Segmentation","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PkuRainBow/OCNet","path":"utils/resnet_block.py","file_url":"https://github.com/PkuRainBow/OCNet/blob/HEAD/utils/resnet_block.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.04242","paper":"/paper/dynamic-group-convolution-for-accelerating","title":"Dynamic Group Convolution for Accelerating Convolutional Neural Networks","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuogege1943/dgc","path":"models/dynamic_resnet.py","file_url":"https://github.com/zhuogege1943/dgc/blob/HEAD/models/dynamic_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2007.04118","paper":"/paper/delving-into-the-adversarial-robustness-on","title":"RobFR: Benchmarking Adversarial Robustness on Face Recognition","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/realsafe","path":"ares/model/resnet.py","file_url":"https://github.com/thu-ml/realsafe/blob/HEAD/ares/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2007.04118","paper":"/paper/delving-into-the-adversarial-robustness-on","title":"RobFR: Benchmarking Adversarial Robustness on Face Recognition","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/realsafe","path":"ares/model/resnet_denoise.py","file_url":"https://github.com/thu-ml/realsafe/blob/HEAD/ares/model/resnet_denoise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c4b3a9d234aede9e","mcp_get_code":{"code_sha256":"c4b3a9d234aede9e"}},{"arxiv_id":"2007.03815","paper":"/paper/real-time-semantic-segmentation-with-fast","title":"Real-time Semantic Segmentation with Fast Attention","date":"2020-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feinanshan/FANet","path":"Testing/models/fanet/resnet.py","file_url":"https://github.com/feinanshan/FANet/blob/HEAD/Testing/models/fanet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.03730","paper":"/paper/detection-as-regression-certified-object","title":"Detection as Regression: Certified Object Detection by Median Smoothing","date":"2020-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ping-C/CertifiedObjectDetection","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/Ping-C/CertifiedObjectDetection/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.02713","paper":"/paper/bbs-net-rgb-d-salient-object-detection-with-a","title":"Bifurcated backbone strategy for RGB-D salient object detection","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyjwuyan/BBS-Net","path":"models/BBSNet_model.py","file_url":"https://github.com/zyjwuyan/BBS-Net/blob/HEAD/models/BBSNet_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2007.02454","paper":"/paper/self-challenging-improves-cross-domain","title":"Self-Challenging Improves Cross-Domain Generalization","date":"2020-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeLightCMU/RSC","path":"ImageNet/resnet.py","file_url":"https://github.com/DeLightCMU/RSC/blob/HEAD/ImageNet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2007.00145","paper":"/paper/modality-agnostic-attention-fusion-for-visual","title":"Modality-Agnostic Attention Fusion for visual search with text feedback","date":"2020-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nashory/rtic-gcn-pytorch","path":"model/resnet.py","file_url":"https://github.com/nashory/rtic-gcn-pytorch/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2006.14154","paper":"/paper/strictly-batch-imitation-learning-by-energy","title":"Strictly Batch Imitation Learning by Energy-based Distribution Matching","date":"2020-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgrathwohl/JEM","path":"train_wrn_ebm.py","file_url":"https://github.com/wgrathwohl/JEM/blob/HEAD/train_wrn_ebm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bf19b9ba510a9e4a","mcp_get_code":{"code_sha256":"bf19b9ba510a9e4a"}},{"arxiv_id":"2006.12245","paper":"/paper/improving-few-shot-visual-classification-with","title":"Enhancing Few-Shot Image Classification with Unlabelled Examples","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plai-group/simple-cnaps","path":"simple-cnaps-src/resnet.py","file_url":"https://github.com/plai-group/simple-cnaps/blob/HEAD/simple-cnaps-src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.12119","paper":"/paper/the-color-out-of-space-learning-self","title":"The color out of space: learning self-supervised representations for Earth Observation imagery","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevinc/TheColorOutOfSpace","path":"Colorization/models/Decoder_utils.py","file_url":"https://github.com/stevinc/TheColorOutOfSpace/blob/HEAD/Colorization/models/Decoder_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1d2f2ac67597346","mcp_get_code":{"code_sha256":"b1d2f2ac67597346"}},{"arxiv_id":"2006.12000","paper":"/paper/self-knowledge-distillation-a-simple-way-for","title":"Self-Knowledge Distillation with Progressive Refinement of Targets","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lgcnsai/ps-kd-pytorch","path":"models/pyramid.py","file_url":"https://github.com/lgcnsai/ps-kd-pytorch/blob/HEAD/models/pyramid.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.12000","paper":"/paper/self-knowledge-distillation-a-simple-way-for","title":"Self-Knowledge Distillation with Progressive Refinement of Targets","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lgcnsai/ps-kd-pytorch","path":"models/preact_resnet.py","file_url":"https://github.com/lgcnsai/ps-kd-pytorch/blob/HEAD/models/preact_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"2006.12000","paper":"/paper/self-knowledge-distillation-a-simple-way-for","title":"Self-Knowledge Distillation with Progressive Refinement of Targets","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lgcnsai/ps-kd-pytorch","path":"models/pyramid_shake_drop.py","file_url":"https://github.com/lgcnsai/ps-kd-pytorch/blob/HEAD/models/pyramid_shake_drop.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"2006.11942","paper":"/paper/generalisation-guarantees-for-continual","title":"Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent","date":"2020-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MehdiAbbanaBennani/continual-learning-ogdplus","path":"models/resnet.py","file_url":"https://github.com/MehdiAbbanaBennani/continual-learning-ogdplus/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2006.11487","paper":"/paper/paying-more-attention-to-snapshots-of","title":"Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation","date":"2020-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lehduong/kesi","path":"cifar/filter_pruning/models/preresnet.py","file_url":"https://github.com/lehduong/kesi/blob/HEAD/cifar/filter_pruning/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.10518","paper":"/paper/improving-post-training-neural-quantization","title":"Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itayhubara/CalibTIP","path":"models/resnet.py","file_url":"https://github.com/itayhubara/CalibTIP/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1775197fd615680a","mcp_get_code":{"code_sha256":"1775197fd615680a"}},{"arxiv_id":"2006.09661","paper":"/paper/implicit-neural-representations-with-periodic","title":"Implicit Neural Representations with Periodic Activation Functions","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vsitzmann/siren","path":"modules.py","file_url":"https://github.com/vsitzmann/siren/blob/HEAD/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46ad0f97079d1fd8","mcp_get_code":{"code_sha256":"46ad0f97079d1fd8"}},{"arxiv_id":"2006.08437","paper":"/paper/depth-uncertainty-in-neural-networks","title":"Depth Uncertainty in Neural Networks","date":"2020-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/DUN","path":"src/DUN/stochastic_img_resnets.py","file_url":"https://github.com/cambridge-mlg/DUN/blob/HEAD/src/DUN/stochastic_img_resnets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.07989","paper":"/paper/gradaug-a-new-regularization-method-for-deep","title":"GradAug: A New Regularization Method for Deep Neural Networks","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taoyang1122/GradAug","path":"models/pyramidnet_randwidth.py","file_url":"https://github.com/taoyang1122/GradAug/blob/HEAD/models/pyramidnet_randwidth.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.07989","paper":"/paper/gradaug-a-new-regularization-method-for-deep","title":"GradAug: A New Regularization Method for Deep Neural Networks","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taoyang1122/GradAug","path":"models/resnet_randdepth.py","file_url":"https://github.com/taoyang1122/GradAug/blob/HEAD/models/resnet_randdepth.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2006.07794","paper":"/paper/patchup-a-regularization-technique-for","title":"PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chandar-lab/PatchUp","path":"models/wide_resnet.py","file_url":"https://github.com/chandar-lab/PatchUp/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2006.07733","paper":"/paper/bootstrap-your-own-latent-a-new-approach-to","title":"Bootstrap your own latent: A new approach to self-supervised Learning","date":"2020-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyi01827/noisy-contrastive","path":"simsiam/model_factory.py","file_url":"https://github.com/liyi01827/noisy-contrastive/blob/HEAD/simsiam/model_factory.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec20f22a185cd708","mcp_get_code":{"code_sha256":"ec20f22a185cd708"}},{"arxiv_id":"2006.06958","paper":"/paper/understanding-the-role-of-training-regimes-in","title":"Understanding the Role of Training Regimes in Continual Learning","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imirzadeh/stable-continual-learning","path":"stable_sgd/models.py","file_url":"https://github.com/imirzadeh/stable-continual-learning/blob/HEAD/stable_sgd/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2006.05734","paper":"/paper/3d-human-mesh-regression-with-dense-1","title":"3D Human Mesh Regression with Dense Correspondence","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zengwang430521/DecoMR","path":"models/layers.py","file_url":"https://github.com/zengwang430521/DecoMR/blob/HEAD/models/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.04139","paper":"/paper/learning-texture-transformer-network-for-1","title":"Learning Texture Transformer Network for Image Super-Resolution","date":"2020-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/TTSR","path":"loss/discriminator.py","file_url":"https://github.com/researchmm/TTSR/blob/HEAD/loss/discriminator.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"2006.04062","paper":"/paper/consistency-regularization-for-certified","title":"Consistency Regularization for Certified Robustness of Smoothed Classifiers","date":"2020-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jh-jeong/smoothing-consistency","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/jh-jeong/smoothing-consistency/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2006.00719","paper":"/paper/adahessian-an-adaptive-second-order-optimizer","title":"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning","date":"2020-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirgholami/adahessian","path":"image_classification/models/resnet.py","file_url":"https://github.com/amirgholami/adahessian/blob/HEAD/image_classification/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2005.10821","paper":"/paper/hierarchical-multi-scale-attention-for","title":"Hierarchical Multi-Scale Attention for Semantic Segmentation","date":"2020-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVIDIA/semantic-segmentation","path":"network/Resnet.py","file_url":"https://github.com/NVIDIA/semantic-segmentation/blob/HEAD/network/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2005.08209","paper":"/paper/learning-individual-speaking-styles-for","title":"Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis","date":"2020-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Rudrabha/Lip2Wav","path":"face_detection/models.py","file_url":"https://github.com/Rudrabha/Lip2Wav/blob/HEAD/face_detection/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"2005.05220","paper":"/paper/iunets-fully-invertible-u-nets-with-learnable","title":"iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling","date":"2020-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"silvandeleemput/memcnn","path":"memcnn/models/resnet.py","file_url":"https://github.com/silvandeleemput/memcnn/blob/HEAD/memcnn/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2005.03823","paper":"/paper/blind-backdoors-in-deep-learning-models","title":"Blind Backdoors in Deep Learning Models","date":"2020-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ebagdasa/backdoors101","path":"models/resnet.py","file_url":"https://github.com/ebagdasa/backdoors101/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2005.03337","paper":"/paper/wavelet-integrated-cnns-for-noise-robust","title":"Wavelet Integrated CNNs for Noise-Robust Image Classification","date":"2020-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"df92f1a5ba14210a","mcp_get_code":{"code_sha256":"df92f1a5ba14210a"}},{"arxiv_id":"2005.02552","paper":"/paper/enhancing-intrinsic-adversarial-robustness","title":"Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder","date":"2020-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2005.02551","paper":"/paper/cascadepsp-toward-class-agnostic-and-very","title":"CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement","date":"2020-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkchengrex/CascadePSP","path":"models/psp/extractors.py","file_url":"https://github.com/hkchengrex/CascadePSP/blob/HEAD/models/psp/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"2005.00953","paper":"/paper/deep-generative-adversarial-residual","title":"Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution","date":"2020-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RaoUmer/SRResCGAN","path":"srrescgan_code_demo/models/SRResCGAN.py","file_url":"https://github.com/RaoUmer/SRResCGAN/blob/HEAD/srrescgan_code_demo/models/SRResCGAN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cebcc41768bc1cd4","mcp_get_code":{"code_sha256":"cebcc41768bc1cd4"}},{"arxiv_id":"2005.00953","paper":"/paper/deep-generative-adversarial-residual","title":"Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution","date":"2020-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RaoUmer/SRResCGAN","path":"training_codes/models/ResDNet.py","file_url":"https://github.com/RaoUmer/SRResCGAN/blob/HEAD/training_codes/models/ResDNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f80f003de17602a","mcp_get_code":{"code_sha256":"1f80f003de17602a"}},{"arxiv_id":"2005.00695","paper":"/paper/on-the-generalization-effects-of-linear","title":"On the Generalization Effects of Linear Transformations in Data Augmentation","date":"2020-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SenWu/dauphin","path":"dauphin/image/models/pyramidnet.py","file_url":"https://github.com/SenWu/dauphin/blob/HEAD/dauphin/image/models/pyramidnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9ce5cddeac7a72b0","mcp_get_code":{"code_sha256":"9ce5cddeac7a72b0"}},{"arxiv_id":"2005.00695","paper":"/paper/on-the-generalization-effects-of-linear","title":"On the Generalization Effects of Linear Transformations in Data Augmentation","date":"2020-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SenWu/dauphin","path":"dauphin/image/models/wide_resnet.py","file_url":"https://github.com/SenWu/dauphin/blob/HEAD/dauphin/image/models/wide_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"056902da11d3f6b0","mcp_get_code":{"code_sha256":"056902da11d3f6b0"}},{"arxiv_id":"2005.00060","paper":"/paper/bridging-mode-connectivity-in-loss-landscapes-1","title":"Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness","date":"2020-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/model-sanitization","path":"Hessian/models/resnet.py","file_url":"https://github.com/IBM/model-sanitization/blob/HEAD/Hessian/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.11627","paper":"/paper/convolution-weight-distribution-assumption","title":"Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning","date":"2020-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bearpaw/pytorch-classification","path":"models/cifar/preresnet.py","file_url":"https://github.com/bearpaw/pytorch-classification/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.10016","paper":"/paper/unsupervised-domain-adaptation-through-inter","title":"Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition","date":"2020-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2004.09548","paper":"/paper/aanet-adaptive-aggregation-network-for","title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haofeixu/aanet","path":"nets/deform.py","file_url":"https://github.com/haofeixu/aanet/blob/HEAD/nets/deform.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2004.09548","paper":"/paper/aanet-adaptive-aggregation-network-for","title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haofeixu/aanet","path":"nets/feature.py","file_url":"https://github.com/haofeixu/aanet/blob/HEAD/nets/feature.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9fba66e062846970","mcp_get_code":{"code_sha256":"9fba66e062846970"}},{"arxiv_id":"2004.09141","paper":"/paper/spatial-action-maps-for-mobile-manipulation","title":"Spatial Action Maps for Mobile Manipulation","date":"2020-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jimmyyhwu/spatial-action-maps","path":"resnet.py","file_url":"https://github.com/jimmyyhwu/spatial-action-maps/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.07464","paper":"/paper/pick-processing-key-information-extraction","title":"PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks","date":"2020-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenwenyu/PICK-pytorch","path":"model/resnet.py","file_url":"https://github.com/wenwenyu/PICK-pytorch/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.05304","paper":"/paper/inter-region-affinity-distillation-for-road","title":"Inter-Region Affinity Distillation for Road Marking Segmentation","date":"2020-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cardwing/Codes-for-IntRA-KD","path":"models/fc_resnet.py","file_url":"https://github.com/cardwing/Codes-for-IntRA-KD/blob/HEAD/models/fc_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83877d60072f32f1","mcp_get_code":{"code_sha256":"83877d60072f32f1"}},{"arxiv_id":"2004.04989","paper":"/paper/improved-residual-networks-for-image-and","title":"Improved Residual Networks for Image and Video Recognition","date":"2020-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iduta/iresnet","path":"models/iresnet.py","file_url":"https://github.com/iduta/iresnet/blob/HEAD/models/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.04989","paper":"/paper/improved-residual-networks-for-image-and","title":"Improved Residual Networks for Image and Video Recognition","date":"2020-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iduta/iresnet","path":"models/iresgroup.py","file_url":"https://github.com/iduta/iresnet/blob/HEAD/models/iresgroup.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a2bdb1c941359c5","mcp_get_code":{"code_sha256":"7a2bdb1c941359c5"}},{"arxiv_id":"2004.03706","paper":"/paper/long-tailed-recognition-using-class-balanced","title":"Long-Tailed Recognition Using Class-Balanced Experts","date":"2020-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ssfootball04/class-balanced-experts","path":"models.py","file_url":"https://github.com/ssfootball04/class-balanced-experts/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.02760","paper":"/paper/guiding-monocular-depth-estimation-using","title":"Guiding Monocular Depth Estimation Using Depth-Attention Volume","date":"2020-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmeetR/Monocular-Depth-Estimation-DAV","path":"src/networks/depth_attention_volume/encoder.py","file_url":"https://github.com/AmeetR/Monocular-Depth-Estimation-DAV/blob/HEAD/src/networks/depth_attention_volume/encoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"2004.01946","paper":"/paper/weakly-supervised-mesh-convolutional-hand","title":"Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild","date":"2020-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EAST-J/Youtubehand","path":"model/resnet.py","file_url":"https://github.com/EAST-J/Youtubehand/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2004.01800","paper":"/paper/temporally-distributed-networks-for-fast","title":"Temporally Distributed Networks for Fast Video Semantic Segmentation","date":"2020-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feinanshan/TDNet","path":"Testing/model/pspnet/resnet.py","file_url":"https://github.com/feinanshan/TDNet/blob/HEAD/Testing/model/pspnet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2004.00288","paper":"/paper/curricularface-adaptive-curriculum-learning","title":"CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition","date":"2020-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HuangYG123/CurricularFace","path":"backbone/model_resnet.py","file_url":"https://github.com/HuangYG123/CurricularFace/blob/HEAD/backbone/model_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"999e0ea90670a179","mcp_get_code":{"code_sha256":"999e0ea90670a179"}},{"arxiv_id":"2003.14323","paper":"/paper/how-useful-is-self-supervised-pretraining-for","title":"How Useful is Self-Supervised Pretraining for Visual Tasks?","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/selfstudy","path":"models/resnet.py","file_url":"https://github.com/princeton-vl/selfstudy/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.14052","paper":"/paper/3d-sketch-aware-semantic-scene-completion-via","title":"3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"charlesCXK/3D-SketchAware-SSC","path":"furnace/base_model/resnet.py","file_url":"https://github.com/charlesCXK/3D-SketchAware-SSC/blob/HEAD/furnace/base_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.13964","paper":"/paper/regularizing-class-wise-predictions-via-self","title":"Regularizing Class-wise Predictions via Self-knowledge Distillation","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"2003.13678","paper":"/paper/designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZHANGHeng19931123/MutualGuide","path":"models/backbone/resnet_backbone.py","file_url":"https://github.com/ZHANGHeng19931123/MutualGuide/blob/HEAD/models/backbone/resnet_backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.13328","paper":"/paper/2003-13328","title":"Strip Pooling: Rethinking Spatial Pooling for Scene Parsing","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Andrew-Qibin/SPNet","path":"models/resnet.py","file_url":"https://github.com/Andrew-Qibin/SPNet/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.13261","paper":"/paper/domain-aware-visual-bias-eliminating-for","title":"Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning","date":"2020-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mboboGO/DVBE","path":"models/dvbe.py","file_url":"https://github.com/mboboGO/DVBE/blob/HEAD/models/dvbe.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.12327","paper":"/paper/an-investigation-into-the-stochasticity-of","title":"An Investigation into the Stochasticity of Batch Whitening","date":"2020-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/StochasticityBW","path":"SBW_Classification_PyTorch/ImageNet/models/resnet.py","file_url":"https://github.com/huangleiBuaa/StochasticityBW/blob/HEAD/SBW_Classification_PyTorch/ImageNet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.12039","paper":"/paper/raft-recurrent-all-pairs-field-transforms-for","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","date":"2020-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"osmr/pytorchcv","path":"pytorchcv/models/raft.py","file_url":"https://github.com/osmr/pytorchcv/blob/HEAD/pytorchcv/models/raft.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f388bc44b6fceae","mcp_get_code":{"code_sha256":"7f388bc44b6fceae"}},{"arxiv_id":"2003.11942","paper":"/paper/towards-backward-compatible-representation","title":"Towards Backward-Compatible Representation Learning","date":"2020-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.11539","paper":"/paper/rethinking-few-shot-image-classification-a","title":"Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?","date":"2020-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.09338","paper":"/paper/selecting-relevant-features-from-a-universal","title":"Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification","date":"2020-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvornikita/SUR","path":"models/resnet18.py","file_url":"https://github.com/dvornikita/SUR/blob/HEAD/models/resnet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.08608","paper":"/paper/depth-potentiality-aware-gated-attention","title":"DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JosephChenHub/DPANet","path":"resnet.py","file_url":"https://github.com/JosephChenHub/DPANet/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.08607","paper":"/paper/unsupervised-domain-adaptation-via-4","title":"Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering","date":"2020-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gorilla-lab-scut/srdc-cvpr2020","path":"models/resnet.py","file_url":"https://github.com/gorilla-lab-scut/srdc-cvpr2020/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.08440","paper":"/paper/synthesize-then-compare-detecting-failures","title":"Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation","date":"2020-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YingdaXia/SynthCP","path":"models/resnet.py","file_url":"https://github.com/YingdaXia/SynthCP/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.08440","paper":"/paper/synthesize-then-compare-detecting-failures","title":"Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation","date":"2020-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YingdaXia/SynthCP","path":"models/deeplab.py","file_url":"https://github.com/YingdaXia/SynthCP/blob/HEAD/models/deeplab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.07853","paper":"/paper/axial-deeplab-stand-alone-axial-attention-for","title":"Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation","date":"2020-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csrhddlam/axial-deeplab","path":"lib/models/resnet.py","file_url":"https://github.com/csrhddlam/axial-deeplab/blob/HEAD/lib/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.07711","paper":"/paper/f-b-alpha-matting","title":"$F$, $B$, Alpha Matting","date":"2020-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MarcoForte/FBA-Matting","path":"networks/resnet_bn.py","file_url":"https://github.com/MarcoForte/FBA-Matting/blob/HEAD/networks/resnet_bn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.07069","paper":"/paper/self-supervised-discovering-of-causal","title":"Self-Supervised Discovering of Interpretable Features for Reinforcement Learning","date":"2020-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shiwj16/SSINet","path":"net_parts/refinenetLW_parts.py","file_url":"https://github.com/shiwj16/SSINet/blob/HEAD/net_parts/refinenetLW_parts.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"2003.06777","paper":"/paper/deepemd-few-shot-image-classification-with","title":"DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning","date":"2020-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"icoz69/DeepEMD","path":"Models/models/resnet.py","file_url":"https://github.com/icoz69/DeepEMD/blob/HEAD/Models/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.05970","paper":"/paper/lidar-guided-small-obstacle-segmentation","title":"LiDAR guided Small obstacle Segmentation","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kzernobog/small_obstacle_discovery","path":"modeling/backbone/drn.py","file_url":"https://github.com/Kzernobog/small_obstacle_discovery/blob/HEAD/modeling/backbone/drn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"2003.05653","paper":"/paper/towards-high-fidelity-3d-face-reconstruction","title":"Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yiyuan1991/3D-Face-GCNs","path":"face_segment.py","file_url":"https://github.com/yiyuan1991/3D-Face-GCNs/blob/HEAD/face_segment.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.04390","paper":"/paper/a-new-meta-baseline-for-few-shot-learning","title":"Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyvius96/few-shot-meta-baseline","path":"meta-dataset/models/resnet.py","file_url":"https://github.com/cyvius96/few-shot-meta-baseline/blob/HEAD/meta-dataset/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.04390","paper":"/paper/a-new-meta-baseline-for-few-shot-learning","title":"Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyvius96/few-shot-meta-baseline","path":"meta-dataset/models/resnet12.py","file_url":"https://github.com/cyvius96/few-shot-meta-baseline/blob/HEAD/meta-dataset/models/resnet12.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"2003.04297","paper":"/paper/improved-baselines-with-momentum-contrastive","title":"Improved Baselines with Momentum Contrastive Learning","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"derrickwang005/mosrep","path":"moco/resnet.py","file_url":"https://github.com/derrickwang005/mosrep/blob/HEAD/moco/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2003.04286","paper":"/paper/manifold-regularization-for-adversarial","title":"Manifold Regularization for Locally Stable Deep Neural Networks","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"charlesjin/adversarial_regularization","path":"models/resnet.py","file_url":"https://github.com/charlesjin/adversarial_regularization/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2003.03836","paper":"/paper/fine-grained-visual-classification-via","title":"Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PRIS-CV/PMG-Progressive-Multi-Granularity-Training","path":"Resnet.py","file_url":"https://github.com/PRIS-CV/PMG-Progressive-Multi-Granularity-Training/blob/HEAD/Resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2003.03780","paper":"/paper/dada-differentiable-automatic-data","title":"DADA: Differentiable Automatic Data Augmentation","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VDIGPKU/DADA","path":"search_gumbel/networks/resnet.py","file_url":"https://github.com/VDIGPKU/DADA/blob/HEAD/search_gumbel/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.03780","paper":"/paper/dada-differentiable-automatic-data","title":"DADA: Differentiable Automatic Data Augmentation","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VDIGPKU/DADA","path":"search_gumbel/networks/wideresnet.py","file_url":"https://github.com/VDIGPKU/DADA/blob/HEAD/search_gumbel/networks/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2003.03780","paper":"/paper/dada-differentiable-automatic-data","title":"DADA: Differentiable Automatic Data Augmentation","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VDIGPKU/DADA","path":"search_gumbel/networks/pyramidnet.py","file_url":"https://github.com/VDIGPKU/DADA/blob/HEAD/search_gumbel/networks/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"2003.03488","paper":"/paper/reactnet-towards-precise-binary-neural","title":"ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions","date":"2020-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuzechun/ReActNet","path":"mobilenet/1_step1/reactnet.py","file_url":"https://github.com/liuzechun/ReActNet/blob/HEAD/mobilenet/1_step1/reactnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.03284","paper":"/paper/tasknorm-rethinking-batch-normalization-for","title":"TaskNorm: Rethinking Batch Normalization for Meta-Learning","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/cnaps","path":"src/resnet.py","file_url":"https://github.com/cambridge-mlg/cnaps/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.02960","paper":"/paper/forgetting-outside-the-box-scrubbing-deep","title":"Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations","date":"2020-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2003.02960","paper":"/paper/forgetting-outside-the-box-scrubbing-deep","title":"Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations","date":"2020-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2003.01908","paper":"/paper/black-box-smoothing-a-provable-defense-for","title":"Denoised Smoothing: A Provable Defense for Pretrained Classifiers","date":"2020-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/blackbox-smoothing","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/microsoft/blackbox-smoothing/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.00707","paper":"/paper/unbiased-mean-teacher-for-cross-domain-object","title":"Unbiased Mean Teacher for Cross-domain Object Detection","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kinredon/umt","path":"lib/model/umt_faster_rcnn/resnet.py","file_url":"https://github.com/kinredon/umt/blob/HEAD/lib/model/umt_faster_rcnn/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2003.00188","paper":"/paper/robust-6d-object-pose-estimation-by-learning","title":"Robust 6D Object Pose Estimation by Learning RGB-D Features","date":"2020-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mentian/object-posenet","path":"lib/extractors.py","file_url":"https://github.com/mentian/object-posenet/blob/HEAD/lib/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"2002.12047","paper":"/paper/understanding-and-enhancing-mixed-sample-data","title":"FMix: Enhancing Mixed Sample Data Augmentation","date":"2020-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"2002.11102","paper":"/paper/on-feature-normalization-and-data","title":"On Feature Normalization and Data Augmentation","date":"2020-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Boyiliee/MoEx","path":"ImageNet/moex_resnet.py","file_url":"https://github.com/Boyiliee/MoEx/blob/HEAD/ImageNet/moex_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"2002.11102","paper":"/paper/on-feature-normalization-and-data","title":"On Feature Normalization and Data Augmentation","date":"2020-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Boyiliee/MoEx","path":"CIFAR/pyramidnet_moex.py","file_url":"https://github.com/Boyiliee/MoEx/blob/HEAD/CIFAR/pyramidnet_moex.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.10120","paper":"/paper/semantic-flow-for-fast-and-accurate-scene","title":"Semantic Flow for Fast and Accurate Scene Parsing","date":"2020-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.09564","paper":"/paper/towards-robust-and-reproducible-active","title":"Towards Robust and Reproducible Active Learning Using Neural Networks","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acl21/deep-active-learning-pytorch","path":"pycls/models/resnet.py","file_url":"https://github.com/acl21/deep-active-learning-pytorch/blob/HEAD/pycls/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"2002.09437","paper":"/paper/calibrating-deep-neural-networks-using-focal","title":"Calibrating Deep Neural Networks using Focal Loss","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"torrvision/focal_calibration","path":"Net/wide_resnet.py","file_url":"https://github.com/torrvision/focal_calibration/blob/HEAD/Net/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"2002.09437","paper":"/paper/calibrating-deep-neural-networks-using-focal","title":"Calibrating Deep Neural Networks using Focal Loss","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"torrvision/focal_calibration","path":"Net/resnet_tiny_imagenet.py","file_url":"https://github.com/torrvision/focal_calibration/blob/HEAD/Net/resnet_tiny_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ace1c98fa5f2cd5","mcp_get_code":{"code_sha256":"2ace1c98fa5f2cd5"}},{"arxiv_id":"2002.08681","paper":"/paper/unsupervised-multi-class-domain-adaptation","title":"Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice","date":"2020-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YBZh/MultiClassDA","path":"models/resnet_McDalNet.py","file_url":"https://github.com/YBZh/MultiClassDA/blob/HEAD/models/resnet_McDalNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.08397","paper":"/paper/jrmot-a-real-time-3d-multi-object-tracker-and","title":"JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset","date":"2020-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"StanfordVL/JRMOT_ROS","path":"src/aligned_reid_model.py","file_url":"https://github.com/StanfordVL/JRMOT_ROS/blob/HEAD/src/aligned_reid_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.06815","paper":"/paper/class-imbalanced-semi-supervised-learning","title":"Class-Imbalanced Semi-Supervised Learning","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MinsungHyun/Class-Imbalanced-Semi-Supervised-Learning","path":"CISSL_cls/lib/wrn.py","file_url":"https://github.com/MinsungHyun/Class-Imbalanced-Semi-Supervised-Learning/blob/HEAD/CISSL_cls/lib/wrn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4917ae1314a3442","mcp_get_code":{"code_sha256":"a4917ae1314a3442"}},{"arxiv_id":"2002.04264","paper":"/paper/the-devil-is-in-the-channels-mutual-channel","title":"The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification","date":"2020-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongliangchang/Mutual-Channel-Loss","path":"CUB-200-2011_ResNet18.py","file_url":"https://github.com/dongliangchang/Mutual-Channel-Loss/blob/HEAD/CUB-200-2011_ResNet18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.03517","paper":"/paper/random-smoothing-might-be-unable-to-certify","title":"Random Smoothing Might be Unable to Certify $\\ell_\\infty$ Robustness for High-Dimensional Images","date":"2020-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongyanz/TRADES-smoothing","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/hongyanz/TRADES-smoothing/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2002.01650","paper":"/paper/concept-whitening-for-interpretable-image","title":"Concept Whitening for Interpretable Image Recognition","date":"2020-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiCHEN96/ConceptWhitening","path":"MODELS/model_resnet.py","file_url":"https://github.com/zhiCHEN96/ConceptWhitening/blob/HEAD/MODELS/model_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2001.07645","paper":"/paper/saunet-shape-attentive-u-net-for","title":"SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation","date":"2020-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bowang-lab/shape-attentive-unet","path":"models/models.py","file_url":"https://github.com/bowang-lab/shape-attentive-unet/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"2001.07645","paper":"/paper/saunet-shape-attentive-u-net-for","title":"SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation","date":"2020-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunjesse/shape-attentive-unet","path":"models/attention_blocks.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/attention_blocks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"ca893f9498143980","mcp_get_code":{"code_sha256":"ca893f9498143980"}},{"arxiv_id":"2001.06838","paper":"/paper/towards-stabilizing-batch-statistics-in-1","title":"Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization","date":"2020-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-model/MABN","path":"cls/networks/resnet.py","file_url":"https://github.com/megvii-model/MABN/blob/HEAD/cls/networks/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1e5865e06cfa8e5d","mcp_get_code":{"code_sha256":"1e5865e06cfa8e5d"}},{"arxiv_id":"2001.06499","paper":"/paper/temporal-interlacing-network","title":"Temporal Interlacing Network","date":"2020-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eynaij/X-Temporal_catdim","path":"x_temporal/models/resnet.py","file_url":"https://github.com/eynaij/X-Temporal_catdim/blob/HEAD/x_temporal/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2001.06001","paper":"/paper/curriculum-labeling-self-paced-pseudo","title":"Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning","date":"2020-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uvavision/Curriculum-Labeling","path":"models/wideresnet.py","file_url":"https://github.com/uvavision/Curriculum-Labeling/blob/HEAD/models/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"2001.06001","paper":"/paper/curriculum-labeling-self-paced-pseudo","title":"Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning","date":"2020-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uvavision/Curriculum-Labeling","path":"models/resnet.py","file_url":"https://github.com/uvavision/Curriculum-Labeling/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2001.04193","paper":"/paper/deep-learning-for-person-re-identification-a","title":"Deep Learning for Person Re-identification: A Survey and Outlook","date":"2020-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zion-king/Deep-Learning-for-Person-Re-identification","path":"modeling/backbones/resnet.py","file_url":"https://github.com/zion-king/Deep-Learning-for-Person-Re-identification/blob/HEAD/modeling/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"2001.03343","paper":"/paper/rtm3d-real-time-monocular-3d-detection-from","title":"RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving","date":"2020-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maudzung/RTM3D","path":"src/models/fpn_resnet.py","file_url":"https://github.com/maudzung/RTM3D/blob/HEAD/src/models/fpn_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2001.01385","paper":"/paper/identifying-and-compensating-for-feature","title":"Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangyongshun/BagofTricks-LT","path":"lib/backbone/oltr_resnet.py","file_url":"https://github.com/zhangyongshun/BagofTricks-LT/blob/HEAD/lib/backbone/oltr_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2001.00705","paper":"/paper/fractional-skipping-towards-finer-grained","title":"Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference","date":"2020-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Torment123/DFS","path":"models.py","file_url":"https://github.com/Torment123/DFS/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e50cc160a435968b","mcp_get_code":{"code_sha256":"e50cc160a435968b"}},{"arxiv_id":"2001.00689","paper":"/paper/a-neural-dirichlet-process-mixture-model-for","title":"A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning","date":"2020-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"soochan-lee/CN-DPM","path":"components/classifier.py","file_url":"https://github.com/soochan-lee/CN-DPM/blob/HEAD/components/classifier.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1912.11370","paper":"/paper/large-scale-learning-of-general-visual","title":"Big Transfer (BiT): General Visual Representation Learning","date":"2019-12-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"batsresearch/taglets","path":"taglets/models/resnetv2.py","file_url":"https://github.com/batsresearch/taglets/blob/HEAD/taglets/models/resnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"1912.07515","paper":"/paper/learning-a-neural-solver-for-multiple-object","title":"Learning a Neural Solver for Multiple Object Tracking","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvl-tum/mot_neural_solver","path":"src/mot_neural_solver/models/resnet.py","file_url":"https://github.com/dvl-tum/mot_neural_solver/blob/HEAD/src/mot_neural_solver/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"1912.07145","paper":"/paper/pyhessian-neural-networks-through-the-lens-of","title":"PyHessian: Neural Networks Through the Lens of the Hessian","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amirgholami/pyhessian","path":"models/resnet.py","file_url":"https://github.com/amirgholami/pyhessian/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1912.04799","paper":"/paper/learning-depth-guided-convolutions-for","title":"Learning Depth-Guided Convolutions for Monocular 3D Object Detection","date":"2019-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/D4LCN","path":"models/resnet.py","file_url":"https://github.com/dingmyu/D4LCN/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1912.03817","paper":"/paper/machine-unlearning","title":"Machine Unlearning","date":"2019-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleverhans-lab/machine-unlearning","path":"architectures/svhn.py","file_url":"https://github.com/cleverhans-lab/machine-unlearning/blob/HEAD/architectures/svhn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1912.03478","paper":"/paper/a-real-time-global-inference-network-for-one","title":"A Real-time Global Inference Network for One-stage Referring Expression Comprehension","date":"2019-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luogen1996/Real-time-Global-Inference-Network","path":"code/fpn_resnet.py","file_url":"https://github.com/luogen1996/Real-time-Global-Inference-Network/blob/HEAD/code/fpn_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1912.03203","paper":"/paper/dynamic-convolutions-exploiting-spatial","title":"Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference","date":"2019-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thomasverelst/dynconv","path":"classification/dynconv/layers.py","file_url":"https://github.com/thomasverelst/dynconv/blob/HEAD/classification/dynconv/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad0171db14050868","mcp_get_code":{"code_sha256":"ad0171db14050868"}},{"arxiv_id":"1912.01857","paper":"/paper/adjusting-decision-boundary-for-class","title":"Adjusting Decision Boundary for Class Imbalanced Learning","date":"2019-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feidfoe/AdjustBnd4Imbalance","path":"models/cifar/preresnet.py","file_url":"https://github.com/feidfoe/AdjustBnd4Imbalance/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1912.00385","paper":"/paper/the-group-loss-for-deep-metric-learning","title":"The Group Loss for Deep Metric Learning","date":"2019-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"laurinwagner/grouploss_plus","path":"net/resnet.py","file_url":"https://github.com/laurinwagner/grouploss_plus/blob/HEAD/net/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ozora-ogino/efficient_backbones","path":"efficient_backbones/g_ghost_regnet.py","file_url":"https://github.com/ozora-ogino/efficient_backbones/blob/HEAD/efficient_backbones/g_ghost_regnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ozora-ogino/efficient_backbones","path":"efficient_backbones/resnet.py","file_url":"https://github.com/ozora-ogino/efficient_backbones/blob/HEAD/efficient_backbones/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e71d6f8569b6b34","mcp_get_code":{"code_sha256":"7e71d6f8569b6b34"}},{"arxiv_id":"1911.11288","paper":"/paper/autolabeling-3d-objects-with-differentiable","title":"Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors","date":"2019-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TRI-ML/sdflabel","path":"networks/resnet_css.py","file_url":"https://github.com/TRI-ML/sdflabel/blob/HEAD/networks/resnet_css.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.09267","paper":"/paper/semantic-hierarchy-emerges-in-deep-generative","title":"Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis","date":"2019-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShenYujun/HiGAN","path":"predictors/scene_wideresnet.py","file_url":"https://github.com/ShenYujun/HiGAN/blob/HEAD/predictors/scene_wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.09070","paper":"/paper/efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SharifElfouly/easy-model-zoo","path":"easy_model_zoo/bisenet/bisenet.py","file_url":"https://github.com/SharifElfouly/easy-model-zoo/blob/HEAD/easy_model_zoo/bisenet/bisenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.08947","paper":"/paper/real-time-scene-text-detection-with","title":"Real-time Scene Text Detection with Differentiable Binarization","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SURFZJY/Real-time-Text-Detection","path":"models/modules/resnet.py","file_url":"https://github.com/SURFZJY/Real-time-Text-Detection/blob/HEAD/models/modules/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.08947","paper":"/paper/real-time-scene-text-detection-with","title":"Real-time Scene Text Detection with Differentiable Binarization","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WenmuZhou/DBNet.pytorch","path":"models/backbone/resnet.py","file_url":"https://github.com/WenmuZhou/DBNet.pytorch/blob/HEAD/models/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.08114","paper":"/paper/neural-network-pruning-with-residual","title":"Neural Network Pruning with Residual-Connections and Limited-Data","date":"2019-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Roll920/CURL","path":"ImageNet/CURL/1_evaluate_filter_importance/model.py","file_url":"https://github.com/Roll920/CURL/blob/HEAD/ImageNet/CURL/1_evaluate_filter_importance/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.07205","paper":"/paper/refit-a-unified-watermark-removal-framework","title":"REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data","date":"2019-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunblaze-ucb/REFIT","path":"models/resnet.py","file_url":"https://github.com/sunblaze-ucb/REFIT/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1911.06487","paper":"/paper/openloris-object-a-dataset-and-benchmark","title":"OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning","date":"2019-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sheqi/Continual_Learning_CV","path":"backbones/resnet.py","file_url":"https://github.com/sheqi/Continual_Learning_CV/blob/HEAD/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.05722","paper":"/paper/momentum-contrast-for-unsupervised-visual","title":"Momentum Contrast for Unsupervised Visual Representation Learning","date":"2019-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HobbitLong/CMC","path":"models/resnet.py","file_url":"https://github.com/HobbitLong/CMC/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.05371","paper":"/paper/self-labelling-via-simultaneous-clustering-1","title":"Self-labelling via simultaneous clustering and representation learning","date":"2019-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1911.05371","paper":"/paper/self-labelling-via-simultaneous-clustering-1","title":"Self-labelling via simultaneous clustering and representation learning","date":"2019-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yukimasano/self-label","path":"models/resnetv1.py","file_url":"https://github.com/yukimasano/self-label/blob/HEAD/models/resnetv1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.04623","paper":"/paper/simpleshot-revisiting-nearest-neighbor","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","date":"2019-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/simple_shot","path":"src/models/ResNet.py","file_url":"https://github.com/mileyan/simple_shot/blob/HEAD/src/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1911.04623","paper":"/paper/simpleshot-revisiting-nearest-neighbor","title":"SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning","date":"2019-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/simple_shot","path":"src/models/WideResNet.py","file_url":"https://github.com/mileyan/simple_shot/blob/HEAD/src/models/WideResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1911.01769","paper":"/paper/visual-privacy-protection-via-mapping","title":"Visual Privacy Protection via Mapping Distortion","date":"2019-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion","path":"models/cifar/preresnet.py","file_url":"https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1910.13321","paper":"/paper/191013321","title":"Semantic Object Accuracy for Generative Text-to-Image Synthesis","date":"2019-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tohinz/multiple-objects-gan","path":"code/clevr/model.py","file_url":"https://github.com/tohinz/multiple-objects-gan/blob/HEAD/code/clevr/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1910.10750","paper":"/paper/6-pack-category-level-6d-pose-tracker-with","title":"6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints","date":"2019-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"j96w/6-PACK","path":"libs/extractors.py","file_url":"https://github.com/j96w/6-PACK/blob/HEAD/libs/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"1910.08761","paper":"/paper/component-attention-guided-face-super","title":"Component Attention Guided Face Super-Resolution Network: CAGFace","date":"2019-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1910.06562","paper":"/paper/compacting-picking-and-growing-for","title":"Compacting, Picking and Growing for Unforgetting Continual Learning","date":"2019-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivclab/CPG","path":"packnet_models/resnet.py","file_url":"https://github.com/ivclab/CPG/blob/HEAD/packnet_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1910.06278","paper":"/paper/distribution-aware-coordinate-representation","title":"Distribution-Aware Coordinate Representation for Human Pose Estimation","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Klawens/SwinPose","path":"lib/models/pose_hrnet.py","file_url":"https://github.com/Klawens/SwinPose/blob/HEAD/lib/models/pose_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1910.04284","paper":"/paper/improved-sample-complexities-for-deep","title":"Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin","date":"2019-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1910.03867","paper":"/paper/loss-surface-sightseeing-by-multi-point","title":"Loss Landscape Sightseeing with Multi-Point Optimization","date":"2019-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"universome/loss-patterns","path":"src/models/resnet.py","file_url":"https://github.com/universome/loss-patterns/blob/HEAD/src/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1910.03648","paper":"/paper/meta-transfer-learning-through-hard-tasks","title":"Meta-Transfer Learning through Hard Tasks","date":"2019-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/meta-transfer-learning","path":"pytorch/models/resnet_mtl.py","file_url":"https://github.com/yaoyao-liu/meta-transfer-learning/blob/HEAD/pytorch/models/resnet_mtl.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1910.03151","paper":"/paper/eca-net-efficient-channel-attention-for-deep","title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","date":"2019-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BangguWu/ECANet","path":"models/eca_resnet.py","file_url":"https://github.com/BangguWu/ECANet/blob/HEAD/models/eca_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.12513","paper":"/paper/learnable-tree-filter-for-structure","title":"Learnable Tree Filter for Structure-preserving Feature Transform","date":"2019-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"StevenGrove/TreeFilter-Torch","path":"furnace/base_model/resnet.py","file_url":"https://github.com/StevenGrove/TreeFilter-Torch/blob/HEAD/furnace/base_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.11957","paper":"/paper/drawing-early-bird-tickets-towards-more","title":"Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks","date":"2019-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.11895","paper":"/paper/joint-task-self-supervised-learning-for","title":"Joint-task Self-supervised Learning for Temporal Correspondence","date":"2019-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liusifei/UVC","path":"libs/resnet.py","file_url":"https://github.com/Liusifei/UVC/blob/HEAD/libs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.11065","paper":"/paper/object-contextual-representations-for","title":"Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation","date":"2019-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HRNet/HRNet-Semantic-Segmentation","path":"lib/models/seg_hrnet.py","file_url":"https://github.com/HRNet/HRNet-Semantic-Segmentation/blob/HEAD/lib/models/seg_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.07229","paper":"/paper/global-aggregation-then-local-distribution-in","title":"Global Aggregation then Local Distribution in Fully Convolutional Networks","date":"2019-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxtGH/GALD-Net","path":"libs/core/operators.py","file_url":"https://github.com/lxtGH/GALD-Net/blob/HEAD/libs/core/operators.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.05983","paper":"/paper/content-aware-unsupervised-deep-homography","title":"Content-Aware Unsupervised Deep Homography Estimation","date":"2019-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JirongZhang/DeepHomography","path":"Oneline-DLTv1/resnet.py","file_url":"https://github.com/JirongZhang/DeepHomography/blob/HEAD/Oneline-DLTv1/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.02005","paper":"/paper/mining-for-dark-matter-substructure-inferring","title":"Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning","date":"2019-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smsharma/mining-for-substructure-lens","path":"inference/models/resnet.py","file_url":"https://github.com/smsharma/mining-for-substructure-lens/blob/HEAD/inference/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.00321","paper":"/paper/deep-mesh-reconstruction-from-single-rgb","title":"Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks","date":"2019-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jnypan/TMNet","path":"auxiliary/resnet.py","file_url":"https://github.com/jnypan/TMNet/blob/HEAD/auxiliary/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1909.00179","paper":"/paper/boundary-aware-feature-propagation-for-scene","title":"Boundary-Aware Feature Propagation for Scene Segmentation","date":"2019-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"henghuiding/BFP","path":"utils/dilated/resnet.py","file_url":"https://github.com/henghuiding/BFP/blob/HEAD/utils/dilated/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.11542","paper":"/paper/satellite-pose-estimation-with-deep-landmark","title":"Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement","date":"2019-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BoChenYS/satellite-pose-estimation","path":"landmark_regression/lib/models/my_hrnet768.py","file_url":"https://github.com/BoChenYS/satellite-pose-estimation/blob/HEAD/landmark_regression/lib/models/my_hrnet768.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.10357","paper":"/paper/bottom-up-higher-resolution-networks-for","title":"HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation","date":"2019-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Darius-Liesis/HRNet-works","path":"lib/models/pose_higher_hrnet.py","file_url":"https://github.com/Darius-Liesis/HRNet-works/blob/HEAD/lib/models/pose_higher_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.09979","paper":"/paper/deephoyer-learning-sparser-neural-network","title":"DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures","date":"2019-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanghr/DeepHoyer","path":"cifar/models/cifar/preresnet.py","file_url":"https://github.com/yanghr/DeepHoyer/blob/HEAD/cifar/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.07919","paper":"/paper/190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","date":"2019-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HRNet/HRNet-Facial-Landmark-Detection","path":"lib/models/hrnet.py","file_url":"https://github.com/HRNet/HRNet-Facial-Landmark-Detection/blob/HEAD/lib/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.07129","paper":"/paper/zero-shot-grounding-of-objects-from-natural","title":"Zero-Shot Grounding of Objects from Natural Language Queries","date":"2019-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TheShadow29/zsgnet-pytorch","path":"code/fpn_resnet.py","file_url":"https://github.com/TheShadow29/zsgnet-pytorch/blob/HEAD/code/fpn_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.06702","paper":"/paper/floor-sp-inverse-cad-for-floorplans-by","title":"Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path","date":"2019-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"woodfrog/floor-sp","path":"floor-sp/models/drn.py","file_url":"https://github.com/woodfrog/floor-sp/blob/HEAD/floor-sp/models/drn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"1908.06647","paper":"/paper/ranet-ranking-attention-network-for-fast","title":"RANet: Ranking Attention Network for Fast Video Object Segmentation","date":"2019-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Storife/RANet","path":"codes/RANet_lib/RANet_resnet_ins.py","file_url":"https://github.com/Storife/RANet/blob/HEAD/codes/RANet_lib/RANet_resnet_ins.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.05932","paper":"/paper/fsgan-subject-agnostic-face-swapping-and","title":"FSGAN: Subject Agnostic Face Swapping and Reenactment","date":"2019-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuvalNirkin/fsgan","path":"fsgan/models/hrnet.py","file_url":"https://github.com/YuvalNirkin/fsgan/blob/HEAD/fsgan/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.05900","paper":"/paper/efficient-and-accurate-arbitrary-shaped-text","title":"Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network","date":"2019-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WenmuZhou/PAN.pytorch","path":"models/modules/resnet.py","file_url":"https://github.com/WenmuZhou/PAN.pytorch/blob/HEAD/models/modules/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1908.05900","paper":"/paper/efficient-and-accurate-arbitrary-shaped-text","title":"Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network","date":"2019-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuch37/pan-pytorch","path":"models/backbone.py","file_url":"https://github.com/liuch37/pan-pytorch/blob/HEAD/models/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.04742","paper":"/paper/online-continual-learning-with-maximally","title":"Online Continual Learning with Maximally Interfered Retrieval","date":"2019-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"optimass/Maximally_Interfered_Retrieval","path":"model.py","file_url":"https://github.com/optimass/Maximally_Interfered_Retrieval/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec20f22a185cd708","mcp_get_code":{"code_sha256":"ec20f22a185cd708"}},{"arxiv_id":"1908.04008","paper":"/paper/instance-enhancement-batch-normalization-an","title":"Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise","date":"2019-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gbup-group/IEBN","path":"models/cifar/iebn_resnet.py","file_url":"https://github.com/gbup-group/IEBN/blob/HEAD/models/cifar/iebn_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.03706","paper":"/paper/exploiting-temporal-consistency-for-real-time","title":"Exploiting temporal consistency for real-time video depth estimation","date":"2019-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weihaox/ST-CLSTM","path":"models/resnet.py","file_url":"https://github.com/weihaox/ST-CLSTM/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1908.02983","paper":"/paper/pseudo-labeling-and-confirmation-bias-in-deep","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","date":"2019-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricArazo/PseudoLabeling","path":"utils_pseudoLab/wideArchitectures.py","file_url":"https://github.com/EricArazo/PseudoLabeling/blob/HEAD/utils_pseudoLab/wideArchitectures.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1907.13315","paper":"/paper/self-training-with-progressive-augmentation","title":"Self-training with progressive augmentation for unsupervised cross-domain person re-identification","date":"2019-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangxinyu-xyz/PAST-ReID","path":"reid/models/resnet.py","file_url":"https://github.com/zhangxinyu-xyz/PAST-ReID/blob/HEAD/reid/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1907.11628","paper":"/paper/unsupervised-learning-for-optical-flow","title":"Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM","date":"2019-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kwanss/PCLNet","path":"models/resnetM.py","file_url":"https://github.com/Kwanss/PCLNet/blob/HEAD/models/resnetM.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1907.11561","paper":"/paper/deep-learning-for-classification-and-severity","title":"Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress","date":"2019-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"esgario/lara2018","path":"classification/architectures/wideresnet.py","file_url":"https://github.com/esgario/lara2018/blob/HEAD/classification/architectures/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1907.11561","paper":"/paper/deep-learning-for-classification-and-severity","title":"Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress","date":"2019-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"esgario/lara2018","path":"classification/architectures/resnet.py","file_url":"https://github.com/esgario/lara2018/blob/HEAD/classification/architectures/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"1907.04371","paper":"/paper/a-stochastic-first-order-method-for-ordered","title":"Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization","date":"2019-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenjikawaguchi/qSGD","path":"cifar10_WideResNet/models/cifar/resnet.py","file_url":"https://github.com/kenjikawaguchi/qSGD/blob/HEAD/cifar10_WideResNet/models/cifar/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1907.02893","paper":"/paper/invariant-risk-minimization","title":"Invariant Risk Minimization","date":"2019-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"katoro8989/irm_variants_calibration","path":"domainbed/networks.py","file_url":"https://github.com/katoro8989/irm_variants_calibration/blob/HEAD/domainbed/networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1907.00208","paper":"/paper/deep-gamblers-learning-to-abstain-with","title":"Deep Gamblers: Learning to Abstain with Portfolio Theory","date":"2019-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Z-T-WANG/NIPS2019DeepGamblers","path":"models/cifar/preresnet.py","file_url":"https://github.com/Z-T-WANG/NIPS2019DeepGamblers/blob/HEAD/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1906.12340","paper":"/paper/using-self-supervised-learning-can-improve","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","date":"2019-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hendrycks/ss-ood","path":"models/resnet.py","file_url":"https://github.com/hendrycks/ss-ood/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1906.10842","paper":"/paper/universal-litmus-patterns-revealing-backdoor","title":"Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs","date":"2019-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UMBCvision/Universal-Litmus-Patterns","path":"tiny-imagenet/resnet.py","file_url":"https://github.com/UMBCvision/Universal-Litmus-Patterns/blob/HEAD/tiny-imagenet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1906.09607","paper":"/paper/densely-connected-search-space-for-more","title":"Densely Connected Search Space for More Flexible Neural Architecture Search","date":"2019-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JaminFong/DenseNAS","path":"models/operations.py","file_url":"https://github.com/JaminFong/DenseNAS/blob/HEAD/models/operations.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1906.05856","paper":"/paper/detecting-photoshopped-faces-by-scripting","title":"Detecting Photoshopped Faces by Scripting Photoshop","date":"2019-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeterWang512/FALdetector","path":"networks/drn.py","file_url":"https://github.com/PeterWang512/FALdetector/blob/HEAD/networks/drn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"1906.05105","paper":"/paper/pose-from-shape-deep-pose-estimation-for","title":"Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YoungXIAO13/PoseFromShape","path":"auxiliary/resnet.py","file_url":"https://github.com/YoungXIAO13/PoseFromShape/blob/HEAD/auxiliary/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1906.04933","paper":"/paper/non-parametric-calibration-for-classification","title":"Non-Parametric Calibration for Classification","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JonathanWenger/pycalib","path":"pycalib/models/cifar100/preresnet.py","file_url":"https://github.com/JonathanWenger/pycalib/blob/HEAD/pycalib/models/cifar100/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1906.04584","paper":"/paper/provably-robust-deep-learning-via","title":"Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers","date":"2019-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azshue/boosting_robust","path":"code/archs/cifar_resnet.py","file_url":"https://github.com/azshue/boosting_robust/blob/HEAD/code/archs/cifar_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1906.04423","paper":"/paper/nas-fcos-fast-neural-architecture-search-for","title":"NAS-FCOS: Fast Neural Architecture Search for Object Detection","date":"2019-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lausannen/NAS-FCOS","path":"maskrcnn_benchmark/nas/modeling/layer_factory.py","file_url":"https://github.com/Lausannen/NAS-FCOS/blob/HEAD/maskrcnn_benchmark/nas/modeling/layer_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"44528dc0396b98a2","mcp_get_code":{"code_sha256":"44528dc0396b98a2"}},{"arxiv_id":"1906.02425","paper":"/paper/uncertainty-guided-continual-learning-with","title":"Uncertainty-guided Continual Learning with Bayesian Neural Networks","date":"2019-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SaynaEbrahimi/UCB","path":"src/networks/resnet_ucb.py","file_url":"https://github.com/SaynaEbrahimi/UCB/blob/HEAD/src/networks/resnet_ucb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c7994f266fbc4ab3","mcp_get_code":{"code_sha256":"c7994f266fbc4ab3"}},{"arxiv_id":"1906.00651","paper":"/paper/190600651","title":"Probabilistic Noise2Void: Unsupervised Content-Aware Denoising","date":"2019-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juglab/pn2v","path":"src/pn2v/unet/model.py","file_url":"https://github.com/juglab/pn2v/blob/HEAD/src/pn2v/unet/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fe1bac001ffc64d5","mcp_get_code":{"code_sha256":"fe1bac001ffc64d5"}},{"arxiv_id":"1906.00555","paper":"/paper/190600555","title":"Adversarially Robust Generalization Just Requires More Unlabeled Data","date":"2019-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RuntianZ/adversarial-robustness-unlabeled","path":"model.py","file_url":"https://github.com/RuntianZ/adversarial-robustness-unlabeled/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"1906.00001","paper":"/paper/190600001","title":"Functional Adversarial Attacks","date":"2019-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cassidylaidlaw/ReColorAdv","path":"recoloradv/mister_ed/cifar10/wide_resnets.py","file_url":"https://github.com/cassidylaidlaw/ReColorAdv/blob/HEAD/recoloradv/mister_ed/cifar10/wide_resnets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1905.13260","paper":"/paper/large-scale-incremental-learning-1","title":"Large Scale Incremental Learning","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.13200","paper":"/paper/exploiting-uncertainty-of-loss-landscape-for","title":"Exploiting Uncertainty of Loss Landscape for Stochastic Optimization","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bsvineethiitg/adams","path":"experiments/models/cifar/preresnet.py","file_url":"https://github.com/bsvineethiitg/adams/blob/HEAD/experiments/models/cifar/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.13143","paper":"/paper/semantics-aligned-representation-learning-for","title":"Semantics-Aligned Representation Learning for Person Re-identification","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification","path":"torchreid/models/resnet.py","file_url":"https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification/blob/HEAD/torchreid/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1905.12794","paper":"/paper/the-fashion-iq-dataset-retrieving-images-by","title":"Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hssip/fashionsap","path":"models/resnet.py","file_url":"https://github.com/hssip/fashionsap/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mayurji/Image-Classification-PyTorch","path":"EfficientNet.py","file_url":"https://github.com/Mayurji/Image-Classification-PyTorch/blob/HEAD/EfficientNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"09ba7db4c4aa871f","mcp_get_code":{"code_sha256":"09ba7db4c4aa871f"}},{"arxiv_id":"1905.11468","paper":"/paper/scaleable-input-gradient-regularization-for","title":"Scaleable input gradient regularization for adversarial robustness","date":"2019-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cfinlay/tulip","path":"imagenet/resnet.py","file_url":"https://github.com/cfinlay/tulip/blob/HEAD/imagenet/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.10841","paper":"/paper/utilizing-automated-breast-cancer-detection","title":"Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer","date":"2019-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SBU-BMI/quip_cancer_segmentation","path":"training/resnet.py","file_url":"https://github.com/SBU-BMI/quip_cancer_segmentation/blob/HEAD/training/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.10671","paper":"/paper/dianet-dense-and-implicit-attention-network","title":"DIANet: Dense-and-Implicit Attention Network","date":"2019-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gbup-group/DIANet","path":"models/cifar/dia_preresnet.py","file_url":"https://github.com/gbup-group/DIANet/blob/HEAD/models/cifar/dia_preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.10671","paper":"/paper/dianet-dense-and-implicit-attention-network","title":"DIANet: Dense-and-Implicit Attention Network","date":"2019-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gbup-group/EAN-efficient-attention-network","path":"search_imagent/imagenet_network/forward_config_dia_fbresnet.py","file_url":"https://github.com/gbup-group/EAN-efficient-attention-network/blob/HEAD/search_imagent/imagenet_network/forward_config_dia_fbresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"1905.09646","paper":"/paper/spatial-group-wise-enhance-improving-semantic","title":"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whai362/PytorchInsight","path":"detection/mmdet/models/backbones/resnet_sge.py","file_url":"https://github.com/whai362/PytorchInsight/blob/HEAD/detection/mmdet/models/backbones/resnet_sge.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"77f89e05c55c985d","mcp_get_code":{"code_sha256":"77f89e05c55c985d"}},{"arxiv_id":"1905.09646","paper":"/paper/spatial-group-wise-enhance-improving-semantic","title":"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whai362/PytorchInsight","path":"classification/models/imagenet/resnet_sge.py","file_url":"https://github.com/whai362/PytorchInsight/blob/HEAD/classification/models/imagenet/resnet_sge.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cddb8eda1eb5217a","mcp_get_code":{"code_sha256":"cddb8eda1eb5217a"}},{"arxiv_id":"1905.06368","paper":"/paper/190506368","title":"Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwydj/ultra_high_resolution_segmentation","path":"models/resnet_dilation.py","file_url":"https://github.com/chenwydj/ultra_high_resolution_segmentation/blob/HEAD/models/resnet_dilation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.05901","paper":"/paper/learning-what-and-where-to-transfer","title":"Learning What and Where to Transfer","date":"2019-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alinlab/L2T-ww","path":"models/modules.py","file_url":"https://github.com/alinlab/L2T-ww/blob/HEAD/models/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.05754","paper":"/paper/190505754","title":"Learnable Triangulation of Human Pose","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karfly/learnable-triangulation-pytorch","path":"mvn/models/pose_resnet.py","file_url":"https://github.com/karfly/learnable-triangulation-pytorch/blob/HEAD/mvn/models/pose_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.03684","paper":"/paper/190503684","title":"Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation","date":"2019-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwein3/jacobian-reg","path":"models/bn_wideresnet.py","file_url":"https://github.com/cwein3/jacobian-reg/blob/HEAD/models/bn_wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sajjjadayobi/FaceLib","path":"facelib/FacialExpression/models/resnet.py","file_url":"https://github.com/sajjjadayobi/FaceLib/blob/HEAD/facelib/FacialExpression/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TinDang97/face_recognition","path":"models/resnet.py","file_url":"https://github.com/TinDang97/face_recognition/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.00397","paper":"/paper/fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/fast-autoaugment","path":"FastAutoAugment/networks/resnet.py","file_url":"https://github.com/kakaobrain/fast-autoaugment/blob/HEAD/FastAutoAugment/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1905.00397","paper":"/paper/fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/fast-autoaugment","path":"FastAutoAugment/networks/pyramidnet.py","file_url":"https://github.com/kakaobrain/fast-autoaugment/blob/HEAD/FastAutoAugment/networks/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"1904.12848","paper":"/paper/unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ildoonet/unsupervised-data-augmentation","path":"networks/wideresnet.py","file_url":"https://github.com/ildoonet/unsupervised-data-augmentation/blob/HEAD/networks/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1904.11943","paper":"/paper/swalp-stochastic-weight-averaging-in-low","title":"SWALP : Stochastic Weight Averaging in Low-Precision Training","date":"2019-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevenygd/SWALP","path":"models/preresnet_low.py","file_url":"https://github.com/stevenygd/SWALP/blob/HEAD/models/preresnet_low.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1904.11486","paper":"/paper/190411486","title":"Making Convolutional Networks Shift-Invariant Again","date":"2019-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1904.10633","paper":"/paper/lffd-a-light-and-fast-face-detector-for-edge","title":"LFFD: A Light and Fast Face Detector for Edge Devices","date":"2019-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"becauseofAI/lffd-pytorch","path":"face_detection/net_farm/naivenet.py","file_url":"https://github.com/becauseofAI/lffd-pytorch/blob/HEAD/face_detection/net_farm/naivenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb23199a3b7b61cd","mcp_get_code":{"code_sha256":"eb23199a3b7b61cd"}},{"arxiv_id":"1904.10620","paper":"/paper/bidirectional-learning-for-domain-adaptation","title":"Bidirectional Learning for Domain Adaptation of Semantic Segmentation","date":"2019-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyunsheng13/BDL","path":"model/deeplab.py","file_url":"https://github.com/liyunsheng13/BDL/blob/HEAD/model/deeplab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.10348","paper":"/paper/monte-carlo-tree-search-for-efficient","title":"Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning","date":"2019-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ylabbe/rearrangement-planning","path":"rearrangement/models/wide_resnet.py","file_url":"https://github.com/ylabbe/rearrangement-planning/blob/HEAD/rearrangement/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1904.09739","paper":"/paper/switchable-whitening-for-deep-representation","title":"Switchable Whitening for Deep Representation Learning","date":"2019-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XingangPan/Switchable-Whitening","path":"models/backbones/resnet.py","file_url":"https://github.com/XingangPan/Switchable-Whitening/blob/HEAD/models/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.09229","paper":"/paper/xlsor-a-robust-and-accurate-lung-segmentor-on","title":"XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation","date":"2019-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1904.09058","paper":"/paper/feature-fusion-for-online-mutual-knowledge","title":"Feature Fusion for Online Mutual Knowledge Distillation","date":"2019-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jangho-Kim/FFL-pytorch","path":"our_network.py","file_url":"https://github.com/Jangho-Kim/FFL-pytorch/blob/HEAD/our_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.08921","paper":"/paper/deep-parametric-shape-predictions-using","title":"Deep Parametric Shape Predictions using Distance Fields","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmsm/DeepParametricShapes","path":"dps_3d/models.py","file_url":"https://github.com/dmsm/DeepParametricShapes/blob/HEAD/dps_3d/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68212b2060dc618d","mcp_get_code":{"code_sha256":"68212b2060dc618d"}},{"arxiv_id":"1904.08479","paper":"/paper/lcc-learning-to-customize-and-combine-neural","title":"An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/E3BM","path":"model/wrn.py","file_url":"https://github.com/yaoyao-liu/E3BM/blob/HEAD/model/wrn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1904.08479","paper":"/paper/lcc-learning-to-customize-and-combine-neural","title":"An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaoyao-liu/E3BM","path":"model/resnet12.py","file_url":"https://github.com/yaoyao-liu/E3BM/blob/HEAD/model/resnet12.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1904.08104","paper":"/paper/rawnet-advanced-end-to-end-deep-neural","title":"RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KrishnaDN/RawNet","path":"model.py","file_url":"https://github.com/KrishnaDN/RawNet/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6f35a6878e6a714","mcp_get_code":{"code_sha256":"d6f35a6878e6a714"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dheerajpreddy/CenterNet-without-DCN","path":"src/lib/models/networks/resnet_dcn.py","file_url":"https://github.com/dheerajpreddy/CenterNet-without-DCN/blob/HEAD/src/lib/models/networks/resnet_dcn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PingoLH/CenterNet-HarDNet","path":"src/lib/models/networks/hardnet.py","file_url":"https://github.com/PingoLH/CenterNet-HarDNet/blob/HEAD/src/lib/models/networks/hardnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1904.07399","paper":"/paper/adaptive-wing-loss-for-robust-face-alignment","title":"Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"affromero/SMILE","path":"metrics/arcface_resnet.py","file_url":"https://github.com/affromero/SMILE/blob/HEAD/metrics/arcface_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.07399","paper":"/paper/adaptive-wing-loss-for-robust-face-alignment","title":"Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zs7779/FAN_AdaptiveWingLoss","path":"core/models.py","file_url":"https://github.com/zs7779/FAN_AdaptiveWingLoss/blob/HEAD/core/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd28335574411d48","mcp_get_code":{"code_sha256":"cd28335574411d48"}},{"arxiv_id":"1904.06487","paper":"/paper/semi-supervised-domain-adaptation-via-minimax","title":"Semi-supervised Domain Adaptation via Minimax Entropy","date":"2019-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/SSDA_MME","path":"model/resnet.py","file_url":"https://github.com/VisionLearningGroup/SSDA_MME/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.05441","paper":"/paper/asvspoof-2019-future-horizons-in-spoofed-and","title":"ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection","date":"2019-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmirmohammadRostami/ASV-anti-spoofing-with-EABN","path":"src/attentionnet.py","file_url":"https://github.com/AmirmohammadRostami/ASV-anti-spoofing-with-EABN/blob/HEAD/src/attentionnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.05049","paper":"/paper/drop-an-octave-reducing-spatial-redundancy-in","title":"Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution","date":"2019-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxtGH/OctaveConv_pytorch","path":"libs/nn/OCtaveResnet.py","file_url":"https://github.com/lxtGH/OctaveConv_pytorch/blob/HEAD/libs/nn/OCtaveResnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5535c2973c06adf","mcp_get_code":{"code_sha256":"e5535c2973c06adf"}},{"arxiv_id":"1904.04514","paper":"/paper/high-resolution-representations-for-labeling","title":"High-Resolution Representations for Labeling Pixels and Regions","date":"2019-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kukby/Mish-semantic-segmentation-pytorch","path":"models/hrnet.py","file_url":"https://github.com/kukby/Mish-semantic-segmentation-pytorch/blob/HEAD/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.04232","paper":"/paper/a-closer-look-at-few-shot-classification-1","title":"A Closer Look at Few-shot Classification","date":"2019-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1904.03441","paper":"/paper/iterative-normalization-beyond","title":"Iterative Normalization: Beyond Standardization towards Efficient Whitening","date":"2019-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/IterNorm-pytorch","path":"ImageNet/models/resnet.py","file_url":"https://github.com/huangleiBuaa/IterNorm-pytorch/blob/HEAD/ImageNet/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.01866","paper":"/paper/a-comprehensive-overhaul-of-feature","title":"A Comprehensive Overhaul of Feature Distillation","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/overhaul-distillation","path":"CIFAR-100/models/PyramidNet.py","file_url":"https://github.com/clovaai/overhaul-distillation/blob/HEAD/CIFAR-100/models/PyramidNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.01693","paper":"/paper/multigrid-predictive-filter-flow-for","title":"Multigrid Predictive Filter Flow for Unsupervised Learning on Videos","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.01538","paper":"/paper/spatial-attentive-single-image-deraining-with","title":"Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chiukin/SPANet","path":"SPANet.py","file_url":"https://github.com/chiukin/SPANet/blob/HEAD/SPANet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6978f1d410660f53","mcp_get_code":{"code_sha256":"6978f1d410660f53"}},{"arxiv_id":"1904.01355","paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ricky40403/Fcos_seg","path":"Fcos_seg/backbone/resnet.py","file_url":"https://github.com/ricky40403/Fcos_seg/blob/HEAD/Fcos_seg/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1904.01355","paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xytpai/fcos","path":"backbone.py","file_url":"https://github.com/xytpai/fcos/blob/HEAD/backbone.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.01355","paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ricky40403/Fcos_seg","path":"Fcos_seg/backbone/volvenet.py","file_url":"https://github.com/ricky40403/Fcos_seg/blob/HEAD/Fcos_seg/backbone/volvenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"971b790fc16af272","mcp_get_code":{"code_sha256":"971b790fc16af272"}},{"arxiv_id":"1904.01169","paper":"/paper/res2net-a-new-multi-scale-backbone","title":"Res2Net: A New Multi-scale Backbone Architecture","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChrisMats/Res2Net","path":"res2net.py","file_url":"https://github.com/ChrisMats/Res2Net/blob/HEAD/res2net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"1904.01160","paper":"/paper/curls-whey-boosting-black-box-adversarial","title":"Curls & Whey: Boosting Black-Box Adversarial Attacks","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"walegahaha/Curls-Whey","path":"fmodels/resnet/resnet101.py","file_url":"https://github.com/walegahaha/Curls-Whey/blob/HEAD/fmodels/resnet/resnet101.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1904.00993","paper":"/paper/equivariant-multi-view-networks","title":"Equivariant Multi-View Networks","date":"2019-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daniilidis-group/emvn","path":"emvn/models.py","file_url":"https://github.com/daniilidis-group/emvn/blob/HEAD/emvn/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22fe4f98c6c78209","mcp_get_code":{"code_sha256":"22fe4f98c6c78209"}},{"arxiv_id":"1904.00830","paper":"/paper/depth-aware-video-frame-interpolation","title":"Depth-Aware Video Frame Interpolation","date":"2019-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baowenbo/DAIN","path":"S2D_models/S2DF.py","file_url":"https://github.com/baowenbo/DAIN/blob/HEAD/S2D_models/S2DF.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2f0d358d3c74e41","mcp_get_code":{"code_sha256":"b2f0d358d3c74e41"}},{"arxiv_id":"1903.12648","paper":"/paper/incremental-learning-with-unlabeled-data-in","title":"Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild","date":"2019-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kibok90/iccv2019-inc","path":"models/resnet.py","file_url":"https://github.com/kibok90/iccv2019-inc/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1903.11412","paper":"/paper/self-supervised-learning-via-conditional","title":"Self-Supervised Learning via Conditional Motion Propagation","date":"2019-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiaohangZhan/conditional-motion-propagation","path":"models/backbone/resnet.py","file_url":"https://github.com/XiaohangZhan/conditional-motion-propagation/blob/HEAD/models/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1903.11233","paper":"/paper/deep-co-training-for-semi-supervised-image-2","title":"Deep Co-Training for Semi-Supervised Image Segmentation","date":"2019-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ETS-Research-Repositories/deep-clustering-toolbox","path":"deepclustering/arch/classification/preresnet.py","file_url":"https://github.com/ETS-Research-Repositories/deep-clustering-toolbox/blob/HEAD/deepclustering/arch/classification/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1903.10829","paper":"/paper/srm-a-style-based-recalibration-module-for","title":"SRM : A Style-based Recalibration Module for Convolutional Neural Networks","date":"2019-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1903.08817","paper":"/paper/dual-residual-networks-leveraging-the","title":"Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration","date":"2019-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liu-vis/DualResidualNetworks","path":"pietorch/se_nets.py","file_url":"https://github.com/liu-vis/DualResidualNetworks/blob/HEAD/pietorch/se_nets.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1903.05625","paper":"/paper/tracking-without-bells-and-whistles","title":"Tracking without bells and whistles","date":"2019-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HoganZhang/mot_neural_solver","path":"src/mot_neural_solver/models/resnet.py","file_url":"https://github.com/HoganZhang/mot_neural_solver/blob/HEAD/src/mot_neural_solver/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"1903.04411","paper":"/paper/stroke-based-artistic-rendering-agent-with","title":"Learning to Paint With Model-based Deep Reinforcement Learning","date":"2019-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzwer/ICCV2019-LearningToPaint","path":"baseline_modelfree/DRL/actor.py","file_url":"https://github.com/hzwer/ICCV2019-LearningToPaint/blob/HEAD/baseline_modelfree/DRL/actor.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1903.04411","paper":"/paper/stroke-based-artistic-rendering-agent-with","title":"Learning to Paint With Model-based Deep Reinforcement Learning","date":"2019-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzwer/ICCV2019-LearningToPaint","path":"baseline_modelfree/DRL/critic.py","file_url":"https://github.com/hzwer/ICCV2019-LearningToPaint/blob/HEAD/baseline_modelfree/DRL/critic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33b1a9d94f4b985d","mcp_get_code":{"code_sha256":"33b1a9d94f4b985d"}},{"arxiv_id":"1903.04197","paper":"/paper/structured-knowledge-distillation-for","title":"Structured Knowledge Distillation for Dense Prediction","date":"2019-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"irfanICMLL/structure_knowledge_distillation","path":"networks/pspnet_combine.py","file_url":"https://github.com/irfanICMLL/structure_knowledge_distillation/blob/HEAD/networks/pspnet_combine.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1903.03238","paper":"/paper/ranked-list-loss-for-deep-metric-learning","title":"Ranked List Loss for Deep Metric Learning","date":"2019-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Qidian213/Ranked_Person_ReID","path":"modeling/backbones/resnet_ibn_a.py","file_url":"https://github.com/Qidian213/Ranked_Person_ReID/blob/HEAD/modeling/backbones/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1903.03238","paper":"/paper/ranked-list-loss-for-deep-metric-learning","title":"Ranked List Loss for Deep Metric Learning","date":"2019-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Qidian213/Ranked_Person_ReID","path":"modeling/backbones/resnet.py","file_url":"https://github.com/Qidian213/Ranked_Person_ReID/blob/HEAD/modeling/backbones/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1903.02351","paper":"/paper/canet-class-agnostic-segmentation-networks","title":"CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning","date":"2019-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1902.09777","paper":"/paper/single-image-piece-wise-planar-3d","title":"Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"svip-lab/PlanarReconstruction","path":"models/resnet_scene.py","file_url":"https://github.com/svip-lab/PlanarReconstruction/blob/HEAD/models/resnet_scene.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1902.09738","paper":"/paper/stereo-r-cnn-based-3d-object-detection-for","title":"Stereo R-CNN based 3D Object Detection for Autonomous Driving","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUST-Aerial-Robotics/Stereo-RCNN","path":"lib/model/stereo_rcnn/resnet.py","file_url":"https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN/blob/HEAD/lib/model/stereo_rcnn/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1902.09738","paper":"/paper/stereo-r-cnn-based-3d-object-detection-for","title":"Stereo R-CNN based 3D Object Detection for Autonomous Driving","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ModelBunker/Stereo-RCNN-PyTorch","path":"lib/model/stereo_rcnn/resnet.py","file_url":"https://github.com/ModelBunker/Stereo-RCNN-PyTorch/blob/HEAD/lib/model/stereo_rcnn/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ac64cb4f26a66ac","mcp_get_code":{"code_sha256":"6ac64cb4f26a66ac"}},{"arxiv_id":"1902.09212","paper":"/paper/deep-high-resolution-representation-learning","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","date":"2019-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/human-pose-estimation.pytorch","path":"lib/models/pose_resnet.py","file_url":"https://github.com/Microsoft/human-pose-estimation.pytorch/blob/HEAD/lib/models/pose_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1902.08153","paper":"/paper/learned-step-size-quantization","title":"Learned Step Size Quantization","date":"2019-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhutmost/lsq-net","path":"model/resnet.py","file_url":"https://github.com/zhutmost/lsq-net/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1902.06426","paper":"/paper/2017-robotic-instrument-segmentation","title":"2017 Robotic Instrument Segmentation Challenge","date":"2019-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ternaus/robot-surgery-segmentation","path":"models.py","file_url":"https://github.com/ternaus/robot-surgery-segmentation/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbbe1940f6850b5c","mcp_get_code":{"code_sha256":"dbbe1940f6850b5c"}},{"arxiv_id":"1902.03368","paper":"/paper/skin-lesion-analysis-toward-melanoma-1","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","date":"2019-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MiguelC23/XAI-Skin-Cancer-Detection-A-Prototype-Based-Deep-Learning-Architecture-with-Non-Expert-Supervision","path":"1CP_BinaryProblem/resnet_features.py","file_url":"https://github.com/MiguelC23/XAI-Skin-Cancer-Detection-A-Prototype-Based-Deep-Learning-Architecture-with-Non-Expert-Supervision/blob/HEAD/1CP_BinaryProblem/resnet_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1902.02476","paper":"/paper/a-simple-baseline-for-bayesian-uncertainty-in","title":"A Simple Baseline for Bayesian Uncertainty in Deep Learning","date":"2019-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjmaddox/swa_gaussian","path":"swag/models/preresnet.py","file_url":"https://github.com/wjmaddox/swa_gaussian/blob/HEAD/swag/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1902.00113","paper":"/paper/episodic-training-for-domain-generalization","title":"Episodic Training for Domain Generalization","date":"2019-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HAHA-DL/Episodic-DG","path":"PACS/resnet_epi_fcr.py","file_url":"https://github.com/HAHA-DL/Episodic-DG/blob/HEAD/PACS/resnet_epi_fcr.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1901.11399","paper":"/paper/equivariant-transformer-networks","title":"Equivariant Transformer Networks","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-futuredata/equivariant-transformers","path":"etn/networks.py","file_url":"https://github.com/stanford-futuredata/equivariant-transformers/blob/HEAD/etn/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6692d23a5f54d8ef","mcp_get_code":{"code_sha256":"6692d23a5f54d8ef"}},{"arxiv_id":"1901.07884","paper":"/paper/consistent-rank-logits-for-ordinal-regression","title":"Rank consistent ordinal regression for neural networks with application to age estimation","date":"2019-01-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Raschka-research-group/coral-cnn","path":"model-code/afad-coral.py","file_url":"https://github.com/Raschka-research-group/coral-cnn/blob/HEAD/model-code/afad-coral.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1901.05761","paper":"/paper/attentive-neural-processes","title":"Attentive Neural Processes","date":"2019-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wyn430/NP_ODE","path":"NP_ODE/Decoder_ODE.py","file_url":"https://github.com/wyn430/NP_ODE/blob/HEAD/NP_ODE/Decoder_ODE.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d89f70014190873d","mcp_get_code":{"code_sha256":"d89f70014190873d"}},{"arxiv_id":"1901.04780","paper":"/paper/densefusion-6d-object-pose-estimation-by","title":"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion","date":"2019-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RiplleYang/DenseFusion","path":"lib/extractors.py","file_url":"https://github.com/RiplleYang/DenseFusion/blob/HEAD/lib/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"1901.00976","paper":"/paper/contrastive-adaptation-network-for","title":"Contrastive Adaptation Network for Unsupervised Domain Adaptation","date":"2019-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation","path":"model/resnet.py","file_url":"https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1812.11800","paper":"/paper/bnn-improved-binary-network-training","title":"Regularized Binary Network Training","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1812.11703","paper":"/paper/siamrpn-evolution-of-siamese-visual-tracking","title":"SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"STVIR/pysot","path":"pysot/models/backbone/resnet_atrous.py","file_url":"https://github.com/STVIR/pysot/blob/HEAD/pysot/models/backbone/resnet_atrous.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"48468b2c75aae583","mcp_get_code":{"code_sha256":"48468b2c75aae583"}},{"arxiv_id":"1812.10366","paper":"/paper/a-poisson-gaussian-denoising-dataset-with","title":"A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images","date":"2018-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csm9493/FBI-Denoiser","path":"core/unet.py","file_url":"https://github.com/csm9493/FBI-Denoiser/blob/HEAD/core/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe1bac001ffc64d5","mcp_get_code":{"code_sha256":"fe1bac001ffc64d5"}},{"arxiv_id":"1812.10366","paper":"/paper/a-poisson-gaussian-denoising-dataset-with","title":"A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images","date":"2018-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bmmi/denoising-fluorescence","path":"denoising/models/unet.py","file_url":"https://github.com/bmmi/denoising-fluorescence/blob/HEAD/denoising/models/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d908c5ef8d3e11d","mcp_get_code":{"code_sha256":"8d908c5ef8d3e11d"}},{"arxiv_id":"1812.10352","paper":"/paper/learning-not-to-learn-training-deep-neural","title":"Learning Not to Learn: Training Deep Neural Networks with Biased Data","date":"2018-12-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1812.10025","paper":"/paper/attention-branch-network-learning-of","title":"Attention Branch Network: Learning of Attention Mechanism for Visual Explanation","date":"2018-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"machine-perception-robotics-group/attention_branch_network","path":"models/cifar/resnet.py","file_url":"https://github.com/machine-perception-robotics-group/attention_branch_network/blob/HEAD/models/cifar/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1812.06264","paper":"/paper/hierarchical-discrete-distribution","title":"Hierarchical Discrete Distribution Decomposition for Match Density Estimation","date":"2018-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucbdrive/hd3","path":"models/dla.py","file_url":"https://github.com/ucbdrive/hd3/blob/HEAD/models/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1812.05262","paper":"/paper/elastic-improving-cnns-with-instance-specific","title":"ELASTIC: Improving CNNs with Dynamic Scaling Policies","date":"2018-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/elastic","path":"models/dla.py","file_url":"https://github.com/allenai/elastic/blob/HEAD/models/dla.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1812.05050","paper":"/paper/fast-online-object-tracking-and-segmentation","title":"Fast Online Object Tracking and Segmentation: A Unifying Approach","date":"2018-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ezelikman/anonymal","path":"experiments/siammask_base/resnet.py","file_url":"https://github.com/ezelikman/anonymal/blob/HEAD/experiments/siammask_base/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1812.03828","paper":"/paper/occupancy-networks-learning-3d-reconstruction","title":"Occupancy Networks: Learning 3D Reconstruction in Function Space","date":"2018-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ThibaultGROUEIX/AtlasNet","path":"model/resnet.py","file_url":"https://github.com/ThibaultGROUEIX/AtlasNet/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1811.12006","paper":"/paper/global-second-order-pooling-convolutional","title":"Global Second-order Pooling Convolutional Networks","date":"2018-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1811.11977","paper":"/paper/dula-net-a-dual-projection-network-for","title":"DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama","date":"2018-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1811.11721","paper":"/paper/ccnet-criss-cross-attention-for-semantic","title":"CCNet: Criss-Cross Attention for Semantic Segmentation","date":"2018-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"speedinghzl/CCNet","path":"networks/ccnet.py","file_url":"https://github.com/speedinghzl/CCNet/blob/HEAD/networks/ccnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1811.10745","paper":"/paper/enresnet-resnet-ensemble-via-the-feynman-kac","title":"ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies","date":"2018-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaoWangMath/EnResNet","path":"ResNet20/main_pgd_enresnet5_20.py","file_url":"https://github.com/BaoWangMath/EnResNet/blob/HEAD/ResNet20/main_pgd_enresnet5_20.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"1811.08383","paper":"/paper/temporal-shift-module-for-efficient-video","title":"TSM: Temporal Shift Module for Efficient Video Understanding","date":"2018-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1811.08188","paper":"/paper/orthographic-feature-transform-for-monocular","title":"Orthographic Feature Transform for Monocular 3D Object Detection","date":"2018-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tom-roddick/oft","path":"oft/model/resnet.py","file_url":"https://github.com/tom-roddick/oft/blob/HEAD/oft/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1811.06533","paper":"/paper/learning-to-predict-the-cosmological","title":"Learning to Predict the Cosmological Structure Formation","date":"2018-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siyucosmo/ML-Recon","path":"Unet/uNet.py","file_url":"https://github.com/siyucosmo/ML-Recon/blob/HEAD/Unet/uNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2520baa2fdabec0","mcp_get_code":{"code_sha256":"b2520baa2fdabec0"}},{"arxiv_id":"1811.02759","paper":"/paper/learning-to-steer-by-mimicking-features-from","title":"Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks","date":"2018-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cardwing/Codes-for-Steering-Control","path":"semantic-segmentation/models/fc_resnet.py","file_url":"https://github.com/cardwing/Codes-for-Steering-Control/blob/HEAD/semantic-segmentation/models/fc_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83877d60072f32f1","mcp_get_code":{"code_sha256":"83877d60072f32f1"}},{"arxiv_id":"1811.01335","paper":"/paper/bi-real-net-binarizing-deep-network-towards","title":"Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance","date":"2018-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1810.13049","paper":"/paper/cooperative-holistic-scene-understanding","title":"Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation","date":"2018-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thusiyuan/cooperative_scene_parsing","path":"models/resnet.py","file_url":"https://github.com/thusiyuan/cooperative_scene_parsing/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1810.12348","paper":"/paper/gather-excite-exploiting-feature-context-in","title":"Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Roypic/Attention_Code","path":"internal_attention_block/FcaNet/facnet.py","file_url":"https://github.com/Roypic/Attention_Code/blob/HEAD/internal_attention_block/FcaNet/facnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1810.11910","paper":"/paper/learning-to-learn-without-forgetting-by","title":"Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mattriemer/mer","path":"model/common.py","file_url":"https://github.com/mattriemer/mer/blob/HEAD/model/common.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1810.05331","paper":"/paper/dynamic-channel-pruning-feature-boosting-and","title":"Dynamic Channel Pruning: Feature Boosting and Suppression","date":"2018-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YOUSIKI/PyTorch-FBS","path":"models/resnet.py","file_url":"https://github.com/YOUSIKI/PyTorch-FBS/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aa53dd1441e0a7d8","mcp_get_code":{"code_sha256":"aa53dd1441e0a7d8"}},{"arxiv_id":"1810.04650","paper":"/paper/multi-task-learning-as-multi-objective","title":"Multi-Task Learning as Multi-Objective Optimization","date":"2018-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IntelVCL/MultiObjectiveOptimization","path":"multi_task/models/pspnet.py","file_url":"https://github.com/IntelVCL/MultiObjectiveOptimization/blob/HEAD/multi_task/models/pspnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"1810.04650","paper":"/paper/multi-task-learning-as-multi-objective","title":"Multi-Task Learning as Multi-Objective Optimization","date":"2018-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IntelVCL/MultiObjectiveOptimization","path":"multi_task/models/resnet_mit.py","file_url":"https://github.com/IntelVCL/MultiObjectiveOptimization/blob/HEAD/multi_task/models/resnet_mit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90e50bc6f1220bdd","mcp_get_code":{"code_sha256":"90e50bc6f1220bdd"}},{"arxiv_id":"1810.04020","paper":"/paper/a-comprehensive-survey-of-deep-learning-for","title":"A Comprehensive Survey of Deep Learning for Image Captioning","date":"2018-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skywolf829/CSE5559_Final_Project","path":"old_CNN/models/hrnet.py","file_url":"https://github.com/skywolf829/CSE5559_Final_Project/blob/HEAD/old_CNN/models/hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1810.00861","paper":"/paper/proxquant-quantized-neural-networks-via","title":"ProxQuant: Quantized Neural Networks via Proximal Operators","date":"2018-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenbai01/ProxQuant","path":"models/resnet.py","file_url":"https://github.com/allenbai01/ProxQuant/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1809.09478","paper":"/paper/taking-a-closer-look-at-domain-shift-category","title":"Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation","date":"2018-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RoyalVane/CLAN","path":"model/CLAN_G.py","file_url":"https://github.com/RoyalVane/CLAN/blob/HEAD/model/CLAN_G.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1809.04766","paper":"/paper/real-time-joint-semantic-segmentation-and","title":"Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations","date":"2018-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AleksBanbur/EE8204---Real-Time-Multi-Task-Learning","path":"model.py","file_url":"https://github.com/AleksBanbur/EE8204---Real-Time-Multi-Task-Learning/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fe0da8bae58287c","mcp_get_code":{"code_sha256":"2fe0da8bae58287c"}},{"arxiv_id":"1809.04766","paper":"/paper/real-time-joint-semantic-segmentation-and","title":"Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations","date":"2018-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DrSleep/multi-task-refinenet","path":"src/models.py","file_url":"https://github.com/DrSleep/multi-task-refinenet/blob/HEAD/src/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5615983908c7aa6c","mcp_get_code":{"code_sha256":"5615983908c7aa6c"}},{"arxiv_id":"1809.00287","paper":"/paper/learning-to-navigate-for-fine-grained","title":"Learning to Navigate for Fine-grained Classification","date":"2018-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangze0930/NTS-Net","path":"core/resnet.py","file_url":"https://github.com/yangze0930/NTS-Net/blob/HEAD/core/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1808.07535","paper":"/paper/learning-hierarchical-semantic-image","title":"Learning Hierarchical Semantic Image Manipulation through Structured Representations","date":"2018-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xcyan/neurips18_hierchical_image_manipulation","path":"models/layer_util.py","file_url":"https://github.com/xcyan/neurips18_hierchical_image_manipulation/blob/HEAD/models/layer_util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e722c9ff0920473","mcp_get_code":{"code_sha256":"0e722c9ff0920473"}},{"arxiv_id":"1808.07535","paper":"/paper/learning-hierarchical-semantic-image","title":"Learning Hierarchical Semantic Image Manipulation through Structured Representations","date":"2018-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xcyan/neurips18_hierchical_image_manipulation","path":"models/Discriminator_NET.py","file_url":"https://github.com/xcyan/neurips18_hierchical_image_manipulation/blob/HEAD/models/Discriminator_NET.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ab5f104f9317376","mcp_get_code":{"code_sha256":"8ab5f104f9317376"}},{"arxiv_id":"1808.00897","paper":"/paper/bisenet-bilateral-segmentation-network-for","title":"BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation","date":"2018-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akinoriosamura/TorchSeg-mirror","path":"furnace/base_model/resnet.py","file_url":"https://github.com/akinoriosamura/TorchSeg-mirror/blob/HEAD/furnace/base_model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1808.00278","paper":"/paper/bi-real-net-enhancing-the-performance-of-1","title":"Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm","date":"2018-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.11590","paper":"/paper/acquisition-of-localization-confidence-for","title":"Acquisition of Localization Confidence for Accurate Object Detection","date":"2018-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSAILVision/unifiedparsing","path":"models/resnet.py","file_url":"https://github.com/CSAILVision/unifiedparsing/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.11590","paper":"/paper/acquisition-of-localization-confidence-for","title":"Acquisition of Localization Confidence for Accurate Object Detection","date":"2018-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSAILVision/unifiedparsing","path":"models/models.py","file_url":"https://github.com/CSAILVision/unifiedparsing/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"1807.10916","paper":"/paper/fine-grained-visual-categorization-using-meta","title":"Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data","date":"2018-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YBZh/MetaFGNet","path":"MetaFGNet_with_Sample_Selection/models/resnet.py","file_url":"https://github.com/YBZh/MetaFGNet/blob/HEAD/MetaFGNet_with_Sample_Selection/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.10221","paper":"/paper/unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SonpKing/semantic-segmentation-pytorch","path":"models/resnet.py","file_url":"https://github.com/SonpKing/semantic-segmentation-pytorch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.10221","paper":"/paper/unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SonpKing/semantic-segmentation-pytorch","path":"models/models.py","file_url":"https://github.com/SonpKing/semantic-segmentation-pytorch/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"1807.10002","paper":"/paper/deep-pictorial-gaze-estimation","title":"Deep Pictorial Gaze Estimation","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiamenwcy/pictorial_net","path":"models/pictorial_net.py","file_url":"https://github.com/xiamenwcy/pictorial_net/blob/HEAD/models/pictorial_net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"1807.09856","paper":"/paper/where-are-the-blobs-counting-by-localization","title":"Where are the Blobs: Counting by Localization with Point Supervision","date":"2018-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ElementAI/LCFCN","path":"lcfcn/networks.py","file_url":"https://github.com/ElementAI/LCFCN/blob/HEAD/lcfcn/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c04da432baaa4b0c","mcp_get_code":{"code_sha256":"c04da432baaa4b0c"}},{"arxiv_id":"1807.09441","paper":"/paper/two-at-once-enhancing-learning-and","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","date":"2018-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jihaoxuanye/MetaPRD","path":"mprd/models/resnet_ibn_a.py","file_url":"https://github.com/jihaoxuanye/MetaPRD/blob/HEAD/mprd/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.07860","paper":"/paper/talking-face-generation-by-adversarially","title":"Talking Face Generation by Adversarially Disentangled Audio-Visual Representation","date":"2018-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hangz-nju-cuhk/Talking-Face-Generation-DAVS","path":"network/FAN_feature_extractor.py","file_url":"https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS/blob/HEAD/network/FAN_feature_extractor.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"1807.06906","paper":"/paper/towards-automated-deep-learning-efficient","title":"Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search","date":"2018-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arberzela/EfficientNAS","path":"workers/arch_space/model/pyramidnet.py","file_url":"https://github.com/arberzela/EfficientNAS/blob/HEAD/workers/arch_space/model/pyramidnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1807.06906","paper":"/paper/towards-automated-deep-learning-efficient","title":"Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search","date":"2018-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arberzela/EfficientNAS","path":"workers/arch_space/model/resnet.py","file_url":"https://github.com/arberzela/EfficientNAS/blob/HEAD/workers/arch_space/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1807.06403","paper":"/paper/iterative-residual-network-for-deep-joint","title":"Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network","date":"2018-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cig-skoltech/deep_demosaick","path":"residual_model_resdnet.py","file_url":"https://github.com/cig-skoltech/deep_demosaick/blob/HEAD/residual_model_resdnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f80f003de17602a","mcp_get_code":{"code_sha256":"1f80f003de17602a"}},{"arxiv_id":"1807.01697","paper":"/paper/benchmarking-neural-network-robustness-to","title":"Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations","date":"2018-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neurai-lab/dsp","path":"DSP/DR-TANet-main/TANet_element.py","file_url":"https://github.com/neurai-lab/dsp/blob/HEAD/DSP/DR-TANet-main/TANet_element.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"1807.00459","paper":"/paper/how-to-backdoor-federated-learning","title":"How To Backdoor Federated Learning","date":"2018-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ebagdasa/backdoor_federated_learning","path":"models/pytorch_resnet.py","file_url":"https://github.com/ebagdasa/backdoor_federated_learning/blob/HEAD/models/pytorch_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1806.05236","paper":"/paper/manifold-mixup-better-representations-by","title":"Manifold Mixup: Better Representations by Interpolating Hidden States","date":"2018-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1806.04606","paper":"/paper/knowledge-distillation-by-on-the-fly-native","title":"Knowledge Distillation by On-the-Fly Native Ensemble","date":"2018-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lan1991Xu/ONE_NeurIPS2018","path":"models/cifar/resnet.py","file_url":"https://github.com/Lan1991Xu/ONE_NeurIPS2018/blob/HEAD/models/cifar/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1806.02559","paper":"/paper/shape-robust-text-detection-with-progressive","title":"Shape Robust Text Detection with Progressive Scale Expansion Network","date":"2018-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"A-ZHANG1/PSENet","path":"models/fpn_resnet.py","file_url":"https://github.com/A-ZHANG1/PSENet/blob/HEAD/models/fpn_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1806.02559","paper":"/paper/shape-robust-text-detection-with-progressive","title":"Shape Robust Text Detection with Progressive Scale Expansion Network","date":"2018-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"li10141110/PSENet-tf2","path":"models/fpn_resnet.py","file_url":"https://github.com/li10141110/PSENet-tf2/blob/HEAD/models/fpn_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7c2df8af7f5531c4","mcp_get_code":{"code_sha256":"7c2df8af7f5531c4"}},{"arxiv_id":"1806.01531","paper":"/paper/tafe-net-task-aware-feature-embeddings-for","title":"Deep Mixture of Experts via Shallow Embedding","date":"2018-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RyanKim17920/DeepMoE","path":"deepResNet.py","file_url":"https://github.com/RyanKim17920/DeepMoE/blob/HEAD/deepResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"1806.01531","paper":"/paper/tafe-net-task-aware-feature-embeddings-for","title":"Deep Mixture of Experts via Shallow Embedding","date":"2018-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RyanKim17920/DeepMoE","path":"ResNet.py","file_url":"https://github.com/RyanKim17920/DeepMoE/blob/HEAD/ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"875b54ff64987752","mcp_get_code":{"code_sha256":"875b54ff64987752"}},{"arxiv_id":"1805.12177","paper":"/paper/why-do-deep-convolutional-networks-generalize","title":"Why do deep convolutional networks generalize so poorly to small image transformations?","date":"2018-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1805.11724","paper":"/paper/rethinking-knowledge-graph-propagation-for","title":"Rethinking Knowledge Graph Propagation for Zero-Shot Learning","date":"2018-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyvius96/adgpm","path":"models/resnet.py","file_url":"https://github.com/cyvius96/adgpm/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.11614","paper":"/paper/deep-learning-under-privileged-information","title":"Deep Learning under Privileged Information Using Heteroscedastic Dropout","date":"2018-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"johnwlambert/dlupi-heteroscedastic-dropout","path":"cnns/base_networks/resnet.py","file_url":"https://github.com/johnwlambert/dlupi-heteroscedastic-dropout/blob/HEAD/cnns/base_networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.11046","paper":"/paper/scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/quantized.pytorch","path":"models/resnet.py","file_url":"https://github.com/eladhoffer/quantized.pytorch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.11046","paper":"/paper/scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/quantized.pytorch","path":"models/resnext.py","file_url":"https://github.com/eladhoffer/quantized.pytorch/blob/HEAD/models/resnext.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"1805.11046","paper":"/paper/scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/quantized.pytorch","path":"models/resnet_quantized.py","file_url":"https://github.com/eladhoffer/quantized.pytorch/blob/HEAD/models/resnet_quantized.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2aae5266d40678b7","mcp_get_code":{"code_sha256":"2aae5266d40678b7"}},{"arxiv_id":"1805.11046","paper":"/paper/scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/quantized.pytorch","path":"models/resnet_quantized_float_bn.py","file_url":"https://github.com/eladhoffer/quantized.pytorch/blob/HEAD/models/resnet_quantized_float_bn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d9a75a675636b11","mcp_get_code":{"code_sha256":"3d9a75a675636b11"}},{"arxiv_id":"1805.11046","paper":"/paper/scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/quantized.pytorch","path":"models/resnet_bwn.py","file_url":"https://github.com/eladhoffer/quantized.pytorch/blob/HEAD/models/resnet_bwn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6c8bce847358ab5","mcp_get_code":{"code_sha256":"e6c8bce847358ab5"}},{"arxiv_id":"1805.09806","paper":"/paper/competitive-collaboration-joint-unsupervised","title":"Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anuragranj/cc","path":"models/DispResNet6.py","file_url":"https://github.com/anuragranj/cc/blob/HEAD/models/DispResNet6.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.08805","paper":"/paper/resource-aware-person-re-identification","title":"Resource Aware Person Re-identification across Multiple Resolutions","date":"2018-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/DARENet","path":"models/dare_resnet.py","file_url":"https://github.com/mileyan/DARENet/blob/HEAD/models/dare_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.07925","paper":"/paper/batch-instance-normalization-for-adaptively","title":"Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyeonseob-nam/Batch-Instance-Normalization","path":"models/resnet.py","file_url":"https://github.com/hyeonseob-nam/Batch-Instance-Normalization/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1805.04635","paper":"/paper/direction-aware-spatial-context-features-for-1","title":"Direction-aware Spatial Context Features for Shadow Detection and Removal","date":"2018-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevewongv/dsc-pytorch","path":"DSC.py","file_url":"https://github.com/stevewongv/dsc-pytorch/blob/HEAD/DSC.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6978f1d410660f53","mcp_get_code":{"code_sha256":"6978f1d410660f53"}},{"arxiv_id":"1805.00907","paper":"/paper/glow-graph-lowering-compiler-techniques-for","title":"Glow: Graph Lowering Compiler Techniques for Neural Networks","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"pytorch/glow","path":"torch_glow/utils/torchvision_fake/resnet.py","file_url":"https://github.com/pytorch/glow/blob/HEAD/torch_glow/utils/torchvision_fake/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1804.09170","paper":"/paper/realistic-evaluation-of-deep-semi-supervised","title":"Realistic Evaluation of Deep Semi-Supervised Learning Algorithms","date":"2018-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siit-vtt/semi-supervised-learning-pytorch","path":"preresnet_sd_cifar.py","file_url":"https://github.com/siit-vtt/semi-supervised-learning-pytorch/blob/HEAD/preresnet_sd_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1804.09160","paper":"/paper/no-metrics-are-perfect-adversarial-reward","title":"No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling","date":"2018-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"littlekobe/AREL","path":"misc/resnet.py","file_url":"https://github.com/littlekobe/AREL/blob/HEAD/misc/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1804.08864","paper":"/paper/learning-to-see-the-invisible-end-to-end","title":"Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation","date":"2018-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apchenstu/SLN-Amodal","path":"modal/models_BCE.py","file_url":"https://github.com/apchenstu/SLN-Amodal/blob/HEAD/modal/models_BCE.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbcb53696bc43ef9","mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"arxiv_id":"1803.10704","paper":"/paper/end-to-end-multi-task-learning-with-attention","title":"End-to-End Multi-Task Learning with Attention","date":"2018-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lorenmt/mtan","path":"im2im_pred/model_resnet_mtan/resnet.py","file_url":"https://github.com/lorenmt/mtan/blob/HEAD/im2im_pred/model_resnet_mtan/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1803.10704","paper":"/paper/end-to-end-multi-task-learning-with-attention","title":"End-to-End Multi-Task Learning with Attention","date":"2018-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lorenmt/mtan","path":"visual_decathlon/model_wrn_mtan.py","file_url":"https://github.com/lorenmt/mtan/blob/HEAD/visual_decathlon/model_wrn_mtan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1803.10082","paper":"/paper/efficient-parametrization-of-multi-domain","title":"Efficient parametrization of multi-domain deep neural networks","date":"2018-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"srebuffi/residual_adapters","path":"models.py","file_url":"https://github.com/srebuffi/residual_adapters/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1803.09845","paper":"/paper/neural-baby-talk","title":"Neural Baby Talk","date":"2018-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiasenlu/NeuralBabyTalk","path":"misc/resnet.py","file_url":"https://github.com/jiasenlu/NeuralBabyTalk/blob/HEAD/misc/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1803.09196","paper":"/paper/learning-type-aware-embeddings-for-fashion","title":"Learning Type-Aware Embeddings for Fashion Compatibility","date":"2018-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mvasil/fashion-compatibility","path":"Resnet_18.py","file_url":"https://github.com/mvasil/fashion-compatibility/blob/HEAD/Resnet_18.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1803.08999","paper":"/paper/layoutnet-reconstructing-the-3d-room-layout","title":"LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zouchuhang/LayoutNetv2","path":"resnet_seg_ae.py","file_url":"https://github.com/zouchuhang/LayoutNetv2/blob/HEAD/resnet_seg_ae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1803.08999","paper":"/paper/layoutnet-reconstructing-the-3d-room-layout","title":"LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunset1995/pytorch-layoutnet","path":"model.py","file_url":"https://github.com/sunset1995/pytorch-layoutnet/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01be3eddbd0689a1","mcp_get_code":{"code_sha256":"01be3eddbd0689a1"}},{"arxiv_id":"1803.08494","paper":"/paper/group-normalization","title":"Group Normalization","date":"2018-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppwwyyxx/GroupNorm-reproduce","path":"ImageNet-ResNet-PyTorch/resnet.py","file_url":"https://github.com/ppwwyyxx/GroupNorm-reproduce/blob/HEAD/ImageNet-ResNet-PyTorch/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1803.07055","paper":"/paper/simple-random-search-provides-a-competitive","title":"Simple random search provides a competitive approach to reinforcement learning","date":"2018-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"billchan226/poar-srl-4-robot","path":"srl_zoo/models/models.py","file_url":"https://github.com/billchan226/poar-srl-4-robot/blob/HEAD/srl_zoo/models/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00d0cc5eadefcd18","mcp_get_code":{"code_sha256":"00d0cc5eadefcd18"}},{"arxiv_id":"1803.05407","paper":"/paper/averaging-weights-leads-to-wider-optima-and","title":"Averaging Weights Leads to Wider Optima and Better Generalization","date":"2018-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timgaripov/swa","path":"models/wide_resnet.py","file_url":"https://github.com/timgaripov/swa/blob/HEAD/models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1803.05407","paper":"/paper/averaging-weights-leads-to-wider-optima-and","title":"Averaging Weights Leads to Wider Optima and Better Generalization","date":"2018-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timgaripov/swa","path":"models/preresnet.py","file_url":"https://github.com/timgaripov/swa/blob/HEAD/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1803.01814","paper":"/paper/norm-matters-efficient-and-accurate","title":"Norm matters: efficient and accurate normalization schemes in deep networks","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/norm_matters","path":"models/resnet.py","file_url":"https://github.com/eladhoffer/norm_matters/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1803.01814","paper":"/paper/norm-matters-efficient-and-accurate","title":"Norm matters: efficient and accurate normalization schemes in deep networks","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/norm_matters","path":"models/resnet_wn.py","file_url":"https://github.com/eladhoffer/norm_matters/blob/HEAD/models/resnet_wn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de763ef960e9ed79","mcp_get_code":{"code_sha256":"de763ef960e9ed79"}},{"arxiv_id":"1803.01814","paper":"/paper/norm-matters-efficient-and-accurate","title":"Norm matters: efficient and accurate normalization schemes in deep networks","date":"2018-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eladhoffer/norm_matters","path":"models/resnet_wn_trelu.py","file_url":"https://github.com/eladhoffer/norm_matters/blob/HEAD/models/resnet_wn_trelu.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26f5006865d53130","mcp_get_code":{"code_sha256":"26f5006865d53130"}},{"arxiv_id":"1803.01207","paper":"/paper/automatic-instrument-segmentation-in-robot","title":"Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning","date":"2018-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dbbe1940f6850b5c","mcp_get_code":{"code_sha256":"dbbe1940f6850b5c"}},{"arxiv_id":"1803.00839","paper":"/paper/pose-robust-face-recognition-via-deep","title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","date":"2018-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"penincillin/DREAM","path":"src/CFP/ResNet.py","file_url":"https://github.com/penincillin/DREAM/blob/HEAD/src/CFP/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1802.10349","paper":"/paper/learning-to-adapt-structured-output-space-for","title":"Learning to Adapt Structured Output Space for Semantic Segmentation","date":"2018-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1802.10026","paper":"/paper/loss-surfaces-mode-connectivity-and-fast","title":"Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs","date":"2018-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tjwhitaker/prune-and-tune-ensembles","path":"src/models/resnet.py","file_url":"https://github.com/tjwhitaker/prune-and-tune-ensembles/blob/HEAD/src/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1802.08960","paper":"/paper/bonnet-an-open-source-training-and-deployment","title":"Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs","date":"2018-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PRBonn/bonnetal","path":"train/backbones/resnet.py","file_url":"https://github.com/PRBonn/bonnetal/blob/HEAD/train/backbones/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5724dd4e1465917a","mcp_get_code":{"code_sha256":"5724dd4e1465917a"}},{"arxiv_id":"1802.06474","paper":"/paper/a-closed-form-solution-to-photorealistic","title":"A Closed-form Solution to Photorealistic Image Stylization","date":"2018-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sleebapaul/AuriaKathi","path":"AttnGAN/model.py","file_url":"https://github.com/sleebapaul/AuriaKathi/blob/HEAD/AttnGAN/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"1802.05668","paper":"/paper/model-compression-via-distillation-and","title":"Model compression via distillation and quantization","date":"2018-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"antspy/quantized_distillation","path":"cnn_models/resnet_kfilters.py","file_url":"https://github.com/antspy/quantized_distillation/blob/HEAD/cnn_models/resnet_kfilters.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1802.05668","paper":"/paper/model-compression-via-distillation-and","title":"Model compression via distillation and quantization","date":"2018-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"antspy/quantized_distillation","path":"cnn_models/wide_resnet.py","file_url":"https://github.com/antspy/quantized_distillation/blob/HEAD/cnn_models/wide_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1802.05591","paper":"/paper/towards-end-to-end-lane-detection-an-instance","title":"Towards End-to-End Lane Detection: an Instance Segmentation Approach","date":"2018-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IrohXu/lanenet-lane-detection-pytorch","path":"model/lanenet/backbone/deeplabv3_plus/resnet_atrous.py","file_url":"https://github.com/IrohXu/lanenet-lane-detection-pytorch/blob/HEAD/model/lanenet/backbone/deeplabv3_plus/resnet_atrous.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd0afd75c819b7e6","mcp_get_code":{"code_sha256":"fd0afd75c819b7e6"}},{"arxiv_id":"1802.04977","paper":"/paper/paraphrasing-complex-network-network","title":"Paraphrasing Complex Network: Network Compression via Factor Transfer","date":"2018-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jangho-Kim/Factor-Transfer-pytorch","path":"our_network.py","file_url":"https://github.com/Jangho-Kim/Factor-Transfer-pytorch/blob/HEAD/our_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1802.02375","paper":"/paper/shakedrop-regularization-for-deep-residual","title":"ShakeDrop Regularization for Deep Residual Learning","date":"2018-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1801.07698","paper":"/paper/arcface-additive-angular-margin-loss-for-deep","title":"ArcFace: Additive Angular Margin Loss for Deep Face Recognition","date":"2018-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1801.06313","paper":"/paper/binaryrelax-a-relaxation-approach-for","title":"BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights","date":"2018-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"1801.05746","paper":"/paper/ternausnet-u-net-with-vgg11-encoder-pre","title":"TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation","date":"2018-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yxinjiang/Unet-for-foreground-segmentation","path":"unet_models.py","file_url":"https://github.com/yxinjiang/Unet-for-foreground-segmentation/blob/HEAD/unet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbbe1940f6850b5c","mcp_get_code":{"code_sha256":"dbbe1940f6850b5c"}},{"arxiv_id":"1801.05746","paper":"/paper/ternausnet-u-net-with-vgg11-encoder-pre","title":"TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation","date":"2018-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ternaus/TernausNet","path":"ternausnet/models.py","file_url":"https://github.com/ternaus/TernausNet/blob/HEAD/ternausnet/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"abf0891ad84332d8","mcp_get_code":{"code_sha256":"abf0891ad84332d8"}},{"arxiv_id":"1801.04381","paper":"/paper/mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jmjeon94/MobileNet-Pytorch","path":"MobileNetV2.py","file_url":"https://github.com/jmjeon94/MobileNet-Pytorch/blob/HEAD/MobileNetV2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"23f65ef554d738eb","mcp_get_code":{"code_sha256":"23f65ef554d738eb"}},{"arxiv_id":"1801.04381","paper":"/paper/mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mayurji/Image-Classification-PyTorch","path":"MobileNetV2.py","file_url":"https://github.com/Mayurji/Image-Classification-PyTorch/blob/HEAD/MobileNetV2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"211377a478024586","mcp_get_code":{"code_sha256":"211377a478024586"}},{"arxiv_id":"1712.01815","paper":"/paper/mastering-chess-and-shogi-by-self-play-with-a","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","date":"2017-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1711.11503","paper":"/paper/convolutional-networks-with-adaptive","title":"Convolutional Networks with Adaptive Inference Graphs","date":"2017-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1711.10485","paper":"/paper/attngan-fine-grained-text-to-image-generation","title":"AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks","date":"2017-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexmotogna/attngan","path":"code/model.py","file_url":"https://github.com/alexmotogna/attngan/blob/HEAD/code/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"1711.01558","paper":"/paper/wasserstein-auto-encoders","title":"Wasserstein Auto-Encoders","date":"2017-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1710.10916","paper":"/paper/stackgan-realistic-image-synthesis-with","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2017-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanzhanggit/StackGAN-Pytorch","path":"code/model.py","file_url":"https://github.com/hanzhanggit/StackGAN-Pytorch/blob/HEAD/code/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1710.10916","paper":"/paper/stackgan-realistic-image-synthesis-with","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2017-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rightlit/StackGAN-v2-rev","path":"code/model.py","file_url":"https://github.com/rightlit/StackGAN-v2-rev/blob/HEAD/code/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"1710.08092","paper":"/paper/vggface2-a-dataset-for-recognising-faces","title":"VGGFace2: A dataset for recognising faces across pose and age","date":"2017-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Dou-Yu-xuan/vggface2-pytorch","path":"models/resnet.py","file_url":"https://github.com/Dou-Yu-xuan/vggface2-pytorch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1710.03077","paper":"/paper/deeper-broader-and-artier-domain","title":"Deeper, Broader and Artier Domain Generalization","date":"2017-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xch-liu/geom-tex-dg","path":"Dassl/dassl/modeling/backbone/resnet.py","file_url":"https://github.com/xch-liu/geom-tex-dg/blob/HEAD/Dassl/dassl/modeling/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1710.00935","paper":"/paper/interpretable-convolutional-neural-networks-2","title":"Interpretable Convolutional Neural Networks","date":"2017-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ada-shen/ICNN","path":"model/resnet_18/resnet_18.py","file_url":"https://github.com/ada-shen/ICNN/blob/HEAD/model/resnet_18/resnet_18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4b3a9d234aede9e","mcp_get_code":{"code_sha256":"c4b3a9d234aede9e"}},{"arxiv_id":"1710.00925","paper":"/paper/fine-grained-head-pose-estimation-without","title":"Fine-Grained Head Pose Estimation Without Keypoints","date":"2017-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yakhyo/head-pose-estimation","path":"models/resnet.py","file_url":"https://github.com/yakhyo/head-pose-estimation/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f22d053fb2c79d3","mcp_get_code":{"code_sha256":"7f22d053fb2c79d3"}},{"arxiv_id":"1709.01507","paper":"/paper/squeeze-and-excitation-networks","title":"Squeeze-and-Excitation Networks","date":"2017-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1707.01629","paper":"/paper/dual-path-networks","title":"Dual Path Networks","date":"2017-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DaikiTanak/manifold_mixup","path":"dpn_mixup.py","file_url":"https://github.com/DaikiTanak/manifold_mixup/blob/HEAD/dpn_mixup.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"1706.06083","paper":"/paper/towards-deep-learning-models-resistant-to","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","date":"2017-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1705.02304","paper":"/paper/deep-speaker-an-end-to-end-neural-speaker","title":"Deep Speaker: an End-to-End Neural Speaker Embedding System","date":"2017-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1703.07332","paper":"/paper/how-far-are-we-from-solving-the-2d-3d-face","title":"How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)","date":"2017-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GuohongLi/face-alignment-pytorch","path":"models/resnet.py","file_url":"https://github.com/GuohongLi/face-alignment-pytorch/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1703.07332","paper":"/paper/how-far-are-we-from-solving-the-2d-3d-face","title":"How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)","date":"2017-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1adrianb/face-alignment","path":"face_alignment/models/fan.py","file_url":"https://github.com/1adrianb/face-alignment/blob/HEAD/face_alignment/models/fan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"1702.02429","paper":"/paper/trainable-greedy-decoding-for-neural-machine","title":"Trainable Greedy Decoding for Neural Machine Translation","date":"2017-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kyunghyuncho/rl-pong","path":"conv.py","file_url":"https://github.com/kyunghyuncho/rl-pong/blob/HEAD/conv.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1612.02646","paper":"/paper/learning-video-object-segmentation-from","title":"Learning Video Object Segmentation from Static Images","date":"2016-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"birdman9390/MetaMaskTrack","path":"training/deeplab_resnet.py","file_url":"https://github.com/birdman9390/MetaMaskTrack/blob/HEAD/training/deeplab_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1612.01105","paper":"/paper/pyramid-scene-parsing-network","title":"Pyramid Scene Parsing Network","date":"2016-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1612.00593","paper":"/paper/pointnet-deep-learning-on-point-sets-for-3d","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","date":"2016-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SBPL-Cruz/perch_pose_sampler","path":"pointnet/extractors.py","file_url":"https://github.com/SBPL-Cruz/perch_pose_sampler/blob/HEAD/pointnet/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"1611.08323","paper":"/paper/full-resolution-residual-networks-for","title":"Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes","date":"2016-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1611.06403","paper":"/paper/deep-outdoor-illumination-estimation","title":"Deep Outdoor Illumination Estimation","date":"2016-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CyxFTS/Scenne-illumination-estimation-from-single-image","path":"NeuralNetwork/loader.py","file_url":"https://github.com/CyxFTS/Scenne-illumination-estimation-from-single-image/blob/HEAD/NeuralNetwork/loader.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Duplums/bhb10k-dl-benchmark","path":"models/resnet.py","file_url":"https://github.com/Duplums/bhb10k-dl-benchmark/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"68212b2060dc618d","mcp_get_code":{"code_sha256":"68212b2060dc618d"}},{"arxiv_id":"1610.02915","paper":"/paper/deep-pyramidal-residual-networks","title":"Deep Pyramidal Residual Networks","date":"2016-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dyhan0920/PyramidNet-PyTorch","path":"PyramidNet.py","file_url":"https://github.com/dyhan0920/PyramidNet-PyTorch/blob/HEAD/PyramidNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1610.02391","paper":"/paper/grad-cam-visual-explanations-from-deep","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","date":"2016-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tarolangner/mri-biometry","path":"cnn/models/net_resnet.py","file_url":"https://github.com/tarolangner/mri-biometry/blob/HEAD/cnn/models/net_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1610.00081","paper":"/paper/deep-spatio-temporal-residual-networks-for","title":"Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction","date":"2016-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BruceBinBoxing/ST-ResNet-Pytorch","path":"st_resnet.py","file_url":"https://github.com/BruceBinBoxing/ST-ResNet-Pytorch/blob/HEAD/st_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3d374db4177f20c","mcp_get_code":{"code_sha256":"f3d374db4177f20c"}},{"arxiv_id":"1609.03605","paper":"/paper/detecting-text-in-natural-image-with","title":"Detecting Text in Natural Image with Connectionist Text Proposal Network","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"courao/ocr.pytorch","path":"recognize/crnn.py","file_url":"https://github.com/courao/ocr.pytorch/blob/HEAD/recognize/crnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"938e4e638f594536","mcp_get_code":{"code_sha256":"938e4e638f594536"}},{"arxiv_id":"1609.01775","paper":"/paper/performance-measures-and-a-data-set-for-multi","title":"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking","date":"2016-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adhirajghosh/rptm_reid","path":"model/resnet.py","file_url":"https://github.com/adhirajghosh/rptm_reid/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1114865f06f0fd","mcp_get_code":{"code_sha256":"dd1114865f06f0fd"}},{"arxiv_id":"1608.08710","paper":"/paper/pruning-filters-for-efficient-convnets","title":"Pruning Filters for Efficient ConvNets","date":"2016-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lehduong/ginp","path":"cifar/filter_pruning/models/preresnet.py","file_url":"https://github.com/lehduong/ginp/blob/HEAD/cifar/filter_pruning/models/preresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1608.05442","paper":"/paper/semantic-understanding-of-scenes-through-the","title":"Semantic Understanding of Scenes through the ADE20K Dataset","date":"2016-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1606.04474","paper":"/paper/learning-to-learn-by-gradient-descent-by","title":"Learning to learn by gradient descent by gradient descent","date":"2016-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwydj/learning-to-learn-by-gradient-descent-by-gradient-descent","path":"resnet_meta.py","file_url":"https://github.com/chenwydj/learning-to-learn-by-gradient-descent-by-gradient-descent/blob/HEAD/resnet_meta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f743b7e6cb381d7e","mcp_get_code":{"code_sha256":"f743b7e6cb381d7e"}},{"arxiv_id":"1606.00915","paper":"/paper/deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"warmspringwinds/pytorch-segmentation-detection","path":"pytorch_segmentation_detection/models/deeplab.py","file_url":"https://github.com/warmspringwinds/pytorch-segmentation-detection/blob/HEAD/pytorch_segmentation_detection/models/deeplab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e2edfa6c37815a7","mcp_get_code":{"code_sha256":"9e2edfa6c37815a7"}},{"arxiv_id":"1605.07146","paper":"/paper/wide-residual-networks","title":"Wide Residual Networks","date":"2016-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thughost2/Padam","path":"models/wideresnet.py","file_url":"https://github.com/thughost2/Padam/blob/HEAD/models/wideresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1605.06409","paper":"/paper/r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princewang1994/RFCN_CoupleNet.pytorch","path":"lib/model/couplenet/resnet_atrous.py","file_url":"https://github.com/princewang1994/RFCN_CoupleNet.pytorch/blob/HEAD/lib/model/couplenet/resnet_atrous.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1605.05396","paper":"/paper/generative-adversarial-text-to-image","title":"Generative Adversarial Text to Image Synthesis","date":"2016-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Abhis-123/TextToImage","path":"stackgan/utils/from_original.py","file_url":"https://github.com/Abhis-123/TextToImage/blob/HEAD/stackgan/utils/from_original.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea864de5c552bc4a","mcp_get_code":{"code_sha256":"ea864de5c552bc4a"}},{"arxiv_id":"1605.02971","paper":"/paper/structured-receptive-fields-in-cnns","title":"Structured Receptive Fields in CNNs","date":"2016-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nkarantzas/quick_draw","path":"quickdraw/code/resnet_one.py","file_url":"https://github.com/nkarantzas/quick_draw/blob/HEAD/quickdraw/code/resnet_one.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1603.05279","paper":"/paper/xnor-net-imagenet-classification-using-binary","title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks","date":"2016-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzj994/Binary-Quantization","path":"EnResNet.py","file_url":"https://github.com/lzj994/Binary-Quantization/blob/HEAD/EnResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"1603.04992","paper":"/paper/unsupervised-cnn-for-single-view-depth","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","date":"2016-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"18fbe72ffac82ee3","mcp_get_code":{"code_sha256":"18fbe72ffac82ee3"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7f22d053fb2c79d3","mcp_get_code":{"code_sha256":"7f22d053fb2c79d3"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jung-jun-uk/mixface","path":"recognition/models/iresnet.py","file_url":"https://github.com/jung-jun-uk/mixface/blob/HEAD/recognition/models/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbsariyildiz/resnet-pytorch","path":"src/resnet.py","file_url":"https://github.com/mbsariyildiz/resnet-pytorch/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bc359191fdd6e179","mcp_get_code":{"code_sha256":"bc359191fdd6e179"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangorwell/NGPlus","path":"imagenet/resnet_ngplus.py","file_url":"https://github.com/yangorwell/NGPlus/blob/HEAD/imagenet/resnet_ngplus.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"437a0bfa2e517741","mcp_get_code":{"code_sha256":"437a0bfa2e517741"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/NeuronAlignment","path":"models/resnet.py","file_url":"https://github.com/IBM/NeuronAlignment/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"12a134e249952e6b","mcp_get_code":{"code_sha256":"12a134e249952e6b"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlvccn/bmtc_transferattackvid","path":"Image/models/ResNet.py","file_url":"https://github.com/mlvccn/bmtc_transferattackvid/blob/HEAD/Image/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c9600a450998870f","mcp_get_code":{"code_sha256":"c9600a450998870f"}},{"arxiv_id":"1511.00363","paper":"/paper/binaryconnect-training-deep-neural-networks","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","date":"2015-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"1507.05717","paper":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","date":"2015-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WenmuZhou/crnn.pytorch","path":"modeling/backbone/resnet_torch.py","file_url":"https://github.com/WenmuZhou/crnn.pytorch/blob/HEAD/modeling/backbone/resnet_torch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1506.01186","paper":"/paper/cyclical-learning-rates-for-training-neural","title":"Cyclical Learning Rates for Training Neural Networks","date":"2015-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"dbbe1940f6850b5c","mcp_get_code":{"code_sha256":"dbbe1940f6850b5c"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PatriciaRodrigues1994/Satellite-Image-Segmentation","path":"model_utils/unet.py","file_url":"https://github.com/PatriciaRodrigues1994/Satellite-Image-Segmentation/blob/HEAD/model_utils/unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f02733aec8895435","mcp_get_code":{"code_sha256":"f02733aec8895435"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"melissande/dhi-segmentation-buildings","path":"RUBV3D2.py","file_url":"https://github.com/melissande/dhi-segmentation-buildings/blob/HEAD/RUBV3D2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c17a348b1ff21f6a","mcp_get_code":{"code_sha256":"c17a348b1ff21f6a"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lRomul/argus-tgs-salt","path":"src/nick_zoo/unet_flex.py","file_url":"https://github.com/lRomul/argus-tgs-salt/blob/HEAD/src/nick_zoo/unet_flex.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"257eaa5661078ec2","mcp_get_code":{"code_sha256":"257eaa5661078ec2"}},{"arxiv_id":"1412.6572","paper":"/paper/explaining-and-harnessing-adversarial","title":"Explaining and Harnessing Adversarial Examples","date":"2014-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amerch/CIFAR100-Training","path":"models/resnet.py","file_url":"https://github.com/amerch/CIFAR100-Training/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1409.1556","paper":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjsong/sspl","path":"sspl_w_pcm/models/sound_net.py","file_url":"https://github.com/zjsong/sspl/blob/HEAD/sspl_w_pcm/models/sound_net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"1409.0575","paper":"/paper/imagenet-large-scale-visual-recognition","title":"ImageNet Large Scale Visual Recognition Challenge","date":"2014-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"y2l/meta-transfer-learning","path":"pytorch/models/resnet_mtl.py","file_url":"https://github.com/y2l/meta-transfer-learning/blob/HEAD/pytorch/models/resnet_mtl.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"1406.2661","paper":"/paper/generative-adversarial-networks","title":"Generative Adversarial Networks","date":"2014-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"openreview_QYPKLGyz7K","paper":null,"title":"arXiv:openreview_QYPKLGyz7K","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"scienceliuc/SaTeen","path":"models/Res.py","file_url":"https://github.com/scienceliuc/SaTeen/blob/HEAD/models/Res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"ijcai2025_0850","paper":null,"title":"arXiv:ijcai2025_0850","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hongyi-Lyu-MQ/SULI","path":"models/models.py","file_url":"https://github.com/Hongyi-Lyu-MQ/SULI/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00e569acd6b45ef0","mcp_get_code":{"code_sha256":"00e569acd6b45ef0"}},{"arxiv_id":"ijcai2025_0850","paper":null,"title":"arXiv:ijcai2025_0850","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hongyi-Lyu-MQ/SULI","path":"models/models.py","file_url":"https://github.com/Hongyi-Lyu-MQ/SULI/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"ijcai2025_0179","paper":null,"title":"arXiv:ijcai2025_0179","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"StuLiu/SCPSeg","path":"semseg/models/WiCoNet.py","file_url":"https://github.com/StuLiu/SCPSeg/blob/HEAD/semseg/models/WiCoNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"ijcai2024_0457","paper":null,"title":"arXiv:ijcai2024_0457","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LonelyMoonDesert/FNR-FL","path":"resnetcifar.py","file_url":"https://github.com/LonelyMoonDesert/FNR-FL/blob/HEAD/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"ijcai2023_0434","paper":null,"title":"arXiv:ijcai2023_0434","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HotanLee/SFA","path":"PreResNet.py","file_url":"https://github.com/HotanLee/SFA/blob/HEAD/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"ijcai2022_0131","paper":null,"title":"arXiv:ijcai2022_0131","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"willinglucky/Exploring-Fourier-Prior-for-Single-Image-Rain-Removal","path":"model.py","file_url":"https://github.com/willinglucky/Exploring-Fourier-Prior-for-Single-Image-Rain-Removal/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"822614ced2dcad7a","mcp_get_code":{"code_sha256":"822614ced2dcad7a"}},{"arxiv_id":"aaai_6622","paper":null,"title":"arXiv:aaai_6622","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ouc-ocean-group/LDPS","path":"lib/backbone/resnet.py","file_url":"https://github.com/ouc-ocean-group/LDPS/blob/HEAD/lib/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_34208","paper":null,"title":"arXiv:aaai_34208","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"qinyongchun/PEARL","path":"backbone/resnet.py","file_url":"https://github.com/qinyongchun/PEARL/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_32254","paper":null,"title":"arXiv:aaai_32254","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HongsinLee/FLAIR","path":"backbone/ResNet18.py","file_url":"https://github.com/HongsinLee/FLAIR/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"aaai_30171","paper":null,"title":"arXiv:aaai_30171","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"JingWu321/DCS-2","path":"dlg/models/resnet.py","file_url":"https://github.com/JingWu321/DCS-2/blob/HEAD/dlg/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"aaai_29654","paper":null,"title":"arXiv:aaai_29654","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zcy866/CSR","path":"model.py","file_url":"https://github.com/zcy866/CSR/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_29519","paper":null,"title":"arXiv:aaai_29519","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wu-dd/DIRK","path":"partial_models/resnet.py","file_url":"https://github.com/wu-dd/DIRK/blob/HEAD/partial_models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_29393","paper":null,"title":"arXiv:aaai_29393","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VGCQ/DSD2","path":"utils/models.py","file_url":"https://github.com/VGCQ/DSD2/blob/HEAD/utils/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"aaai_29368","paper":null,"title":"arXiv:aaai_29368","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zibinpan/FedLF","path":"gfedplat/model/NFResNet.py","file_url":"https://github.com/zibinpan/FedLF/blob/HEAD/gfedplat/model/NFResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e7265b0620e96eb","mcp_get_code":{"code_sha256":"0e7265b0620e96eb"}},{"arxiv_id":"aaai_29262","paper":null,"title":"arXiv:aaai_29262","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Keke921/H2T","path":"models/resnet.py","file_url":"https://github.com/Keke921/H2T/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_29250","paper":null,"title":"arXiv:aaai_29250","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Feng-peng-Li/Regroup-Loss-Median-to-Combat-Label-Noise","path":"networks/ResNet.py","file_url":"https://github.com/Feng-peng-Li/Regroup-Loss-Median-to-Combat-Label-Noise/blob/HEAD/networks/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"aaai_29231","paper":null,"title":"arXiv:aaai_29231","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MIV-XJTU/EvoPrompt","path":"inclearn/convnet/my_resnet2.py","file_url":"https://github.com/MIV-XJTU/EvoPrompt/blob/HEAD/inclearn/convnet/my_resnet2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ed2841a05feb6a0","mcp_get_code":{"code_sha256":"2ed2841a05feb6a0"}},{"arxiv_id":"aaai_29079","paper":null,"title":"arXiv:aaai_29079","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"fanyan0411/DSGD","path":"convs/resnet.py","file_url":"https://github.com/fanyan0411/DSGD/blob/HEAD/convs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_29079","paper":null,"title":"arXiv:aaai_29079","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"fanyan0411/DSGD","path":"convs/resnet_cbam.py","file_url":"https://github.com/fanyan0411/DSGD/blob/HEAD/convs/resnet_cbam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_29079","paper":null,"title":"arXiv:aaai_29079","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"fanyan0411/DSGD","path":"convs/modified_represnet.py","file_url":"https://github.com/fanyan0411/DSGD/blob/HEAD/convs/modified_represnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1907f2ae25449f39","mcp_get_code":{"code_sha256":"1907f2ae25449f39"}},{"arxiv_id":"aaai_29068","paper":null,"title":"arXiv:aaai_29068","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NJUyued/SoC4SS-FGVC","path":"models/nets/resnet.py","file_url":"https://github.com/NJUyued/SoC4SS-FGVC/blob/HEAD/models/nets/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_29063","paper":null,"title":"arXiv:aaai_29063","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sjtudyq/FedConcat","path":"resnetcifar.py","file_url":"https://github.com/sjtudyq/FedConcat/blob/HEAD/resnetcifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_28784","paper":null,"title":"arXiv:aaai_28784","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hansong-Zhang/M3D","path":"models/resnet.py","file_url":"https://github.com/Hansong-Zhang/M3D/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_28322","paper":null,"title":"arXiv:aaai_28322","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tsj-001/AAAI24-GKT","path":"gkt/model_bank/big_resnet.py","file_url":"https://github.com/tsj-001/AAAI24-GKT/blob/HEAD/gkt/model_bank/big_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"aaai_28166","paper":null,"title":"arXiv:aaai_28166","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ru1ven/KeypointFusion","path":"model/centerNet.py","file_url":"https://github.com/ru1ven/KeypointFusion/blob/HEAD/model/centerNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_28166","paper":null,"title":"arXiv:aaai_28166","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ru1ven/KeypointFusion","path":"model/resnet.py","file_url":"https://github.com/ru1ven/KeypointFusion/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"aaai_28150","paper":null,"title":"arXiv:aaai_28150","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lhrrrrrr/DiDA","path":"resnet.py","file_url":"https://github.com/lhrrrrrr/DiDA/blob/HEAD/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_28123","paper":null,"title":"arXiv:aaai_28123","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sugar-fly/VSFormer","path":"models/resnet34.py","file_url":"https://github.com/sugar-fly/VSFormer/blob/HEAD/models/resnet34.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_28109","paper":null,"title":"arXiv:aaai_28109","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YanjingLi0202/Bi-ViT","path":"patchconvnet_models.py","file_url":"https://github.com/YanjingLi0202/Bi-ViT/blob/HEAD/patchconvnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ba6aa5f07daca9cd","mcp_get_code":{"code_sha256":"ba6aa5f07daca9cd"}},{"arxiv_id":"aaai_28092","paper":null,"title":"arXiv:aaai_28092","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lhf12278/FCM-ReID","path":"clustercontrast/models/resnet_ibn_a.py","file_url":"https://github.com/lhf12278/FCM-ReID/blob/HEAD/clustercontrast/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_26699","paper":null,"title":"arXiv:aaai_26699","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"fuenwang/MixFairFace","path":"MixFairFace/IResNet.py","file_url":"https://github.com/fuenwang/MixFairFace/blob/HEAD/MixFairFace/IResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"aaai_26161","paper":null,"title":"arXiv:aaai_26161","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NeurAI-Lab/SCoMMER","path":"backbone/ResNet18.py","file_url":"https://github.com/NeurAI-Lab/SCoMMER/blob/HEAD/backbone/ResNet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c2989ace7c5c0da","mcp_get_code":{"code_sha256":"4c2989ace7c5c0da"}},{"arxiv_id":"aaai_25383","paper":null,"title":"arXiv:aaai_25383","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PRIS-CV/Bi-FRN","path":"models/backbones/ResNet.py","file_url":"https://github.com/PRIS-CV/Bi-FRN/blob/HEAD/models/backbones/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"aaai_25216","paper":null,"title":"arXiv:aaai_25216","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ZYN-1101/DandR","path":"defrcn/evaluation/archs/resnet.py","file_url":"https://github.com/ZYN-1101/DandR/blob/HEAD/defrcn/evaluation/archs/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_20232","paper":null,"title":"arXiv:aaai_20232","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zhanxinrui/HDN","path":"hdn/models/backbone/resnet_atrous.py","file_url":"https://github.com/zhanxinrui/HDN/blob/HEAD/hdn/models/backbone/resnet_atrous.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"48468b2c75aae583","mcp_get_code":{"code_sha256":"48468b2c75aae583"}},{"arxiv_id":"aaai_20104","paper":null,"title":"arXiv:aaai_20104","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"swords123/SSC-6D","path":"lib/models/extractors.py","file_url":"https://github.com/swords123/SSC-6D/blob/HEAD/lib/models/extractors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"aaai_20057","paper":null,"title":"arXiv:aaai_20057","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jackie840129/FedFR","path":"backbones/iresnet.py","file_url":"https://github.com/jackie840129/FedFR/blob/HEAD/backbones/iresnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"aaai_20056","paper":null,"title":"arXiv:aaai_20056","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SpadeLiu/Lac-GwcNet","path":"networks/refinement.py","file_url":"https://github.com/SpadeLiu/Lac-GwcNet/blob/HEAD/networks/refinement.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fba66e062846970","mcp_get_code":{"code_sha256":"9fba66e062846970"}},{"arxiv_id":"aaai_19970","paper":null,"title":"arXiv:aaai_19970","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LunarShen/SECRET","path":"secret/models/resnet.py","file_url":"https://github.com/LunarShen/SECRET/blob/HEAD/secret/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"aaai_19890","paper":null,"title":"arXiv:aaai_19890","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"caolinfeng/OoDHDR-codec","path":"compressai/layers/layers.py","file_url":"https://github.com/caolinfeng/OoDHDR-codec/blob/HEAD/compressai/layers/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0cbeed6985b2cf30","mcp_get_code":{"code_sha256":"0cbeed6985b2cf30"}},{"arxiv_id":"aaai_16993","paper":null,"title":"arXiv:aaai_16993","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MJ1021/kcm-code","path":"KCM_implementation_01152021/Multi/models/resnet.py","file_url":"https://github.com/MJ1021/kcm-code/blob/HEAD/KCM_implementation_01152021/Multi/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"aaai_16469","paper":null,"title":"arXiv:aaai_16469","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ZJULearning/resa","path":"models/resnet.py","file_url":"https://github.com/ZJULearning/resa/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"aaai_16465","paper":null,"title":"arXiv:aaai_16465","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zengqunzhao/EfficientFace","path":"models/EfficientFace.py","file_url":"https://github.com/zengqunzhao/EfficientFace/blob/HEAD/models/EfficientFace.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"aaai_16351","paper":null,"title":"arXiv:aaai_16351","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"taoxvzi/DPFPS","path":"models/resnet.py","file_url":"https://github.com/taoxvzi/DPFPS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper","paper":null,"title":"arXiv:Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Impression2805/RCL","path":"model/resnet.py","file_url":"https://github.com/Impression2805/RCL/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"Zhou_XNet_Wavelet-Based_Low_and_High_Frequency_Fusion_Networks_for_Fully-_ICCV_2023_paper","paper":null,"title":"arXiv:Zhou_XNet_Wavelet-Based_Low_and_High_Frequency_Fusion_Networks_for_Fully-_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yanfeng-Zhou/XNet","path":"models/networks_2d/xnet.py","file_url":"https://github.com/Yanfeng-Zhou/XNet/blob/HEAD/models/networks_2d/xnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Zheng_Teeth_Reconstruction_and_Performance_Capture_Using_a_Phone_Camera_ICCV_2025_paper","paper":null,"title":"arXiv:Zheng_Teeth_Reconstruction_and_Performance_Capture_Using_a_Phone_Camera_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hhj1897/face_alignment","path":"ibug/face_alignment/fan/fan.py","file_url":"https://github.com/hhj1897/face_alignment/blob/HEAD/ibug/face_alignment/fan/fan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5baaa8c1b148ef70","mcp_get_code":{"code_sha256":"5baaa8c1b148ef70"}},{"arxiv_id":"Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper","paper":null,"title":"arXiv:Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"doubleZ0108/GeoMVSNet","path":"models/geometry.py","file_url":"https://github.com/doubleZ0108/GeoMVSNet/blob/HEAD/models/geometry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b489862be0d5e045","mcp_get_code":{"code_sha256":"b489862be0d5e045"}},{"arxiv_id":"Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper","paper":null,"title":"arXiv:Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xuzhang1199/CLAMP","path":"mmcv/mmcv/cnn/resnet.py","file_url":"https://github.com/xuzhang1199/CLAMP/blob/HEAD/mmcv/mmcv/cnn/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b257c85a5945d5eb","mcp_get_code":{"code_sha256":"b257c85a5945d5eb"}},{"arxiv_id":"Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","paper":null,"title":"arXiv:Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yuanssr/StyleSRN","path":"model/resnet.py","file_url":"https://github.com/Yuanssr/StyleSRN/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Yu_LaPE_Layer-adaptive_Position_Embedding_for_Vision_Transformers_with_Independent_Layer_ICCV_2023_paper","paper":null,"title":"arXiv:Yu_LaPE_Layer-adaptive_Position_Embedding_for_Vision_Transformers_with_Independent_Layer_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Ingrid725/LaPE","path":"patchconvnet_models.py","file_url":"https://github.com/Ingrid725/LaPE/blob/HEAD/patchconvnet_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"ba6aa5f07daca9cd","mcp_get_code":{"code_sha256":"ba6aa5f07daca9cd"}},{"arxiv_id":"Yang_Stealthy_Backdoor_Attack_in_Federated_Learning_via_Adaptive_Layer-wise_Gradient_ICCV_2025_paper","paper":null,"title":"arXiv:Yang_Stealthy_Backdoor_Attack_in_Federated_Learning_via_Adaptive_Layer-wise_Gradient_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yqqhyqq/LGA","path":"models/resnet_tinyimagenet.py","file_url":"https://github.com/yqqhyqq/LGA/blob/HEAD/models/resnet_tinyimagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper","paper":null,"title":"arXiv:Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hangxu-cv/cvpr24acm","path":"nets/resnet_dcn_DFPN.py","file_url":"https://github.com/hangxu-cv/cvpr24acm/blob/HEAD/nets/resnet_dcn_DFPN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Wu_ProTeCt_Prompt_Tuning_for_Taxonomic_Open_Set_Classification_CVPR_2024_paper","paper":null,"title":"arXiv:Wu_ProTeCt_Prompt_Tuning_for_Taxonomic_Open_Set_Classification_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"gina9726/ProTeCt","path":"models/resnet.py","file_url":"https://github.com/gina9726/ProTeCt/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Wu_Improving_Transferable_Targeted_Adversarial_Attacks_with_Model_Self-Enhancement_CVPR_2024_paper","paper":null,"title":"arXiv:Wu_Improving_Transferable_Targeted_Adversarial_Attacks_with_Model_Self-Enhancement_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"g4alllf/SASD","path":"src/GN/resnet50_GN.py","file_url":"https://github.com/g4alllf/SASD/blob/HEAD/src/GN/resnet50_GN.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","paper":null,"title":"arXiv:Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HaitaoWen/CLearning","path":"model/modified_resnet_cifar.py","file_url":"https://github.com/HaitaoWen/CLearning/blob/HEAD/model/modified_resnet_cifar.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","paper":null,"title":"arXiv:Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HaitaoWen/CLearning","path":"model/model_task_il.py","file_url":"https://github.com/HaitaoWen/CLearning/blob/HEAD/model/model_task_il.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","paper":null,"title":"arXiv:Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HaitaoWen/CLearning","path":"model/uncustomized/resnet.py","file_url":"https://github.com/HaitaoWen/CLearning/blob/HEAD/model/uncustomized/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bde80d96c1e287d8","mcp_get_code":{"code_sha256":"bde80d96c1e287d8"}},{"arxiv_id":"Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper","paper":null,"title":"arXiv:Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"huiyiwang01/SEMA-CL","path":"backbone/resnet.py","file_url":"https://github.com/huiyiwang01/SEMA-CL/blob/HEAD/backbone/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Wang_Practical_Network_Acceleration_With_Tiny_Sets_CVPR_2023_paper","paper":null,"title":"arXiv:Wang_Practical_Network_Acceleration_With_Tiny_Sets_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"DoctorKey/Practise","path":"models/resnet.py","file_url":"https://github.com/DoctorKey/Practise/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"WYC-321/MCF","path":"code/networks/resnet.py","file_url":"https://github.com/WYC-321/MCF/blob/HEAD/code/networks/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e14a56fb15bfea1f","mcp_get_code":{"code_sha256":"e14a56fb15bfea1f"}},{"arxiv_id":"Wang_Learning_To_Learn_and_Remember_Super_Long_Multi-Domain_Task_Sequence_CVPR_2022_paper","paper":null,"title":"arXiv:Wang_Learning_To_Learn_and_Remember_Super_Long_Multi-Domain_Task_Sequence_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"joey-wang123/SDML","path":"model.py","file_url":"https://github.com/joey-wang123/SDML/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a96935ee89a7588f","mcp_get_code":{"code_sha256":"a96935ee89a7588f"}},{"arxiv_id":"Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Gavinwxy/DGCL","path":"dgcl/models/resnet.py","file_url":"https://github.com/Gavinwxy/DGCL/blob/HEAD/dgcl/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Tang_Progressive_Attention_on_Multi-Level_Dense_Difference_Maps_for_Generic_Event_CVPR_2022_paper","paper":null,"title":"arXiv:Tang_Progressive_Attention_on_Multi-Level_Dense_Difference_Maps_for_Generic_Event_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MCG-NJU/DDM","path":"DDM-Net/modeling/resnetGEBD.py","file_url":"https://github.com/MCG-NJU/DDM/blob/HEAD/DDM-Net/modeling/resnetGEBD.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Sun_Unleashing_the_Power_of_Gradient_Signal-to-Noise_Ratio_for_Zero-Shot_NAS_ICCV_2023_paper","paper":null,"title":"arXiv:Sun_Unleashing_the_Power_of_Gradient_Signal-to-Noise_Ratio_for_Zero-Shot_NAS_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Sunzh1996/Xi-GSNR","path":"Xi_GSNR_Consistency/models/ImageNet_ResNet.py","file_url":"https://github.com/Sunzh1996/Xi-GSNR/blob/HEAD/Xi_GSNR_Consistency/models/ImageNet_ResNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d07c7f84a871188","mcp_get_code":{"code_sha256":"8d07c7f84a871188"}},{"arxiv_id":"Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper","paper":null,"title":"arXiv:Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yeh-yu-hsuan/BiFuse","path":"models/resnet.py","file_url":"https://github.com/Yeh-yu-hsuan/BiFuse/blob/HEAD/models/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec301b481c002a8c","mcp_get_code":{"code_sha256":"ec301b481c002a8c"}},{"arxiv_id":"Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper","paper":null,"title":"arXiv:Seidenschwarz_Simple_Cues_Lead_to_a_Strong_Multi-Object_Tracker_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dvl-tum/GHOST","path":"ReID/net/resnet.py","file_url":"https://github.com/dvl-tum/GHOST/blob/HEAD/ReID/net/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Quetu_LaCoOT_Layer_Collapse_through_Optimal_Transport_ICCV_2025_paper","paper":null,"title":"arXiv:Quetu_LaCoOT_Layer_Collapse_through_Optimal_Transport_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VGCQ/LaCoOT","path":"models/ResNet.py","file_url":"https://github.com/VGCQ/LaCoOT/blob/HEAD/models/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"29df79c9fdb0cee8","mcp_get_code":{"code_sha256":"29df79c9fdb0cee8"}},{"arxiv_id":"Phoo_Coarsely-Labeled_Data_for_Better_Few-Shot_Transfer_ICCV_2021_paper","paper":null,"title":"arXiv:Phoo_Coarsely-Labeled_Data_for_Better_Few-Shot_Transfer_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cpphoo/PAS","path":"models/resnet.py","file_url":"https://github.com/cpphoo/PAS/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper","paper":null,"title":"arXiv:Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"luluppang/BCU","path":"models/resnet.py","file_url":"https://github.com/luluppang/BCU/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper","paper":null,"title":"arXiv:Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"asudahkzj/Wnet","path":"models/segmentation.py","file_url":"https://github.com/asudahkzj/Wnet/blob/HEAD/models/segmentation.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper","paper":null,"title":"arXiv:Ning_Trap_Attention_Monocular_Depth_Estimation_With_Manual_Traps_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ICSResearch/TrapAttention","path":"depth/models/trap.py","file_url":"https://github.com/ICSResearch/TrapAttention/blob/HEAD/depth/models/trap.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5706f3c45bae7ef","mcp_get_code":{"code_sha256":"b5706f3c45bae7ef"}},{"arxiv_id":"Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper","paper":null,"title":"arXiv:Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mmendiet/FedAlign","path":"models/resnet.py","file_url":"https://github.com/mmendiet/FedAlign/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Ma_Aligning_Logits_Generatively_for_Principled_Black-Box_Knowledge_Distillation_CVPR_2024_paper","paper":null,"title":"arXiv:Ma_Aligning_Logits_Generatively_for_Principled_Black-Box_Knowledge_Distillation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HAIV-Lab/MEKD","path":"project/resnet.py","file_url":"https://github.com/HAIV-Lab/MEKD/blob/HEAD/project/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Liu_The_Devil_Is_in_the_Margin_Margin-Based_Label_Smoothing_for_CVPR_2022_paper","paper":null,"title":"arXiv:Liu_The_Devil_Is_in_the_Margin_Margin-Based_Label_Smoothing_for_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"by-liu/MbLS","path":"calibrate/net/resnet.py","file_url":"https://github.com/by-liu/MbLS/blob/HEAD/calibrate/net/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper","paper":null,"title":"arXiv:Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yyliu01/PS-MT","path":"CityCode/Model/Deeplabv3_plus/resnet.py","file_url":"https://github.com/yyliu01/PS-MT/blob/HEAD/CityCode/Model/Deeplabv3_plus/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper","paper":null,"title":"arXiv:Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"XixiLiu95/GEN","path":"resnetv2.py","file_url":"https://github.com/XixiLiu95/GEN/blob/HEAD/resnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f01b8d3901f0b289","mcp_get_code":{"code_sha256":"f01b8d3901f0b289"}},{"arxiv_id":"Liu_Class_Adaptive_Network_Calibration_CVPR_2023_paper","paper":null,"title":"arXiv:Liu_Class_Adaptive_Network_Calibration_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"by-liu/CALS","path":"calibrate/net/resnet_tiny_imagenet.py","file_url":"https://github.com/by-liu/CALS/blob/HEAD/calibrate/net/resnet_tiny_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ace1c98fa5f2cd5","mcp_get_code":{"code_sha256":"2ace1c98fa5f2cd5"}},{"arxiv_id":"Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper","paper":null,"title":"arXiv:Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"salesforce/RRL","path":"architectures/resnet.py","file_url":"https://github.com/salesforce/RRL/blob/HEAD/architectures/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper","paper":null,"title":"arXiv:Li_Learning_From_Noisy_Data_With_Robust_Representation_Learning_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"salesforce/RRL","path":"architectures/PreResNet.py","file_url":"https://github.com/salesforce/RRL/blob/HEAD/architectures/PreResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper","paper":null,"title":"arXiv:Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LixiaoTHU/LAAT","path":"models/ResNet12_embedding.py","file_url":"https://github.com/LixiaoTHU/LAAT/blob/HEAD/models/ResNet12_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper","paper":null,"title":"arXiv:Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LixiaoTHU/LAAT","path":"models/resnet12.py","file_url":"https://github.com/LixiaoTHU/LAAT/blob/HEAD/models/resnet12.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5b7c79b0b62af96","mcp_get_code":{"code_sha256":"e5b7c79b0b62af96"}},{"arxiv_id":"Li_FCC_Feature_Clusters_Compression_for_Long-Tailed_Visual_Recognition_CVPR_2023_paper","paper":null,"title":"arXiv:Li_FCC_Feature_Clusters_Compression_for_Long-Tailed_Visual_Recognition_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lijian16/FCC","path":"lib/backbone/oltr_resnet.py","file_url":"https://github.com/lijian16/FCC/blob/HEAD/lib/backbone/oltr_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Li_Dynamic_Dual_Gating_Neural_Networks_ICCV_2021_paper","paper":null,"title":"arXiv:Li_Dynamic_Dual_Gating_Neural_Networks_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lfr-0531/DGNet","path":"models/cifar/resdg.py","file_url":"https://github.com/lfr-0531/DGNet/blob/HEAD/models/cifar/resdg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Lazarou_Iterative_Label_Cleaning_for_Transductive_and_Semi-Supervised_Few-Shot_Learning_ICCV_2021_paper","paper":null,"title":"arXiv:Lazarou_Iterative_Label_Cleaning_for_Transductive_and_Semi-Supervised_Few-Shot_Learning_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MichalisLazarou/iLPC","path":"ici_models/resnet12.py","file_url":"https://github.com/MichalisLazarou/iLPC/blob/HEAD/ici_models/resnet12.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"374871fec9714908","mcp_get_code":{"code_sha256":"374871fec9714908"}},{"arxiv_id":"Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","paper":null,"title":"arXiv:Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lgcnsai/PS-KD-Pytorch","path":"models/pyramid.py","file_url":"https://github.com/lgcnsai/PS-KD-Pytorch/blob/HEAD/models/pyramid.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","paper":null,"title":"arXiv:Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lgcnsai/PS-KD-Pytorch","path":"models/preact_resnet.py","file_url":"https://github.com/lgcnsai/PS-KD-Pytorch/blob/HEAD/models/preact_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e18ed73aa89f051a","mcp_get_code":{"code_sha256":"e18ed73aa89f051a"}},{"arxiv_id":"Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","paper":null,"title":"arXiv:Kim_Self-Knowledge_Distillation_With_Progressive_Refinement_of_Targets_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lgcnsai/PS-KD-Pytorch","path":"models/pyramid_shake_drop.py","file_url":"https://github.com/lgcnsai/PS-KD-Pytorch/blob/HEAD/models/pyramid_shake_drop.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03a0be4fb7381bd2","mcp_get_code":{"code_sha256":"03a0be4fb7381bd2"}},{"arxiv_id":"Kang_A_Soft_Nearest-Neighbor_Framework_for_Continual_Semi-Supervised_Learning_ICCV_2023_paper","paper":null,"title":"arXiv:Kang_A_Soft_Nearest-Neighbor_Framework_for_Continual_Semi-Supervised_Learning_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kangzhiq/NNCSL","path":"src/resnet.py","file_url":"https://github.com/kangzhiq/NNCSL/blob/HEAD/src/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper","paper":null,"title":"arXiv:Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"D1noFuzi/BiasBed","path":"biasedbed/algorithms/SagNet/algorithm.py","file_url":"https://github.com/D1noFuzi/BiasBed/blob/HEAD/biasedbed/algorithms/SagNet/algorithm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper","paper":null,"title":"arXiv:Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jiwei0921/DCF","path":"DCF_code/model/ResNet.py","file_url":"https://github.com/jiwei0921/DCF/blob/HEAD/DCF_code/model/ResNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper","paper":null,"title":"arXiv:Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"IST-DASLab/sparse-imagenet-transfer","path":"models/resnet_imagenet.py","file_url":"https://github.com/IST-DASLab/sparse-imagenet-transfer/blob/HEAD/models/resnet_imagenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper","paper":null,"title":"arXiv:Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shiyuanh/TANE","path":"architectures/ResNetFeat.py","file_url":"https://github.com/shiyuanh/TANE/blob/HEAD/architectures/ResNetFeat.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper","paper":null,"title":"arXiv:Huang_Systematic_Comparison_of_Semi-supervised_and_Self-supervised_Learning_for_Medical_Image_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tufts-ml/SSL-vs-SSL-benchmark","path":"backbone/resnet18_comatch.py","file_url":"https://github.com/tufts-ml/SSL-vs-SSL-benchmark/blob/HEAD/backbone/resnet18_comatch.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Hajimiri_A_Strong_Baseline_for_Generalized_Few-Shot_Semantic_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sinahmr/DIaM","path":"src/model/resnet.py","file_url":"https://github.com/sinahmr/DIaM/blob/HEAD/src/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023_paper","paper":null,"title":"arXiv:Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"guoyongcs/RSPC","path":"RSPC_FAN/models/fan.py","file_url":"https://github.com/guoyongcs/RSPC/blob/HEAD/RSPC_FAN/models/fan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53606449adac233f","mcp_get_code":{"code_sha256":"53606449adac233f"}},{"arxiv_id":"Garrido-Munoz_On_the_Generalization_of_Handwritten_Text_Recognition_Models_CVPR_2025_paper","paper":null,"title":"arXiv:Garrido-Munoz_On_the_Generalization_of_Handwritten_Text_Recognition_Models_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"carlos10garrido/HTR-OOD","path":"src/models/components/htr_vit.py","file_url":"https://github.com/carlos10garrido/HTR-OOD/blob/HEAD/src/models/components/htr_vit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"583f9780bdd00a45","mcp_get_code":{"code_sha256":"583f9780bdd00a45"}},{"arxiv_id":"Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper","paper":null,"title":"arXiv:Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VITA-Group/CADTransformer","path":"models/seg_hrnet.py","file_url":"https://github.com/VITA-Group/CADTransformer/blob/HEAD/models/seg_hrnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Deng_MemSAM_Taming_Segment_Anything_Model_for_Echocardiography_Video_Segmentation_CVPR_2024_paper","paper":null,"title":"arXiv:Deng_MemSAM_Taming_Segment_Anything_Model_for_Echocardiography_Video_Segmentation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dengxl0520/MemSAM","path":"models/segment_anything_memsam/modeling/resnet.py","file_url":"https://github.com/dengxl0520/MemSAM/blob/HEAD/models/segment_anything_memsam/modeling/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48f5a5ec1d5dd2ef","mcp_get_code":{"code_sha256":"48f5a5ec1d5dd2ef"}},{"arxiv_id":"Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper","paper":null,"title":"arXiv:Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ywz978020607/HESIC","path":"compressai/layers/layers.py","file_url":"https://github.com/ywz978020607/HESIC/blob/HEAD/compressai/layers/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fce10771ab1d00db","mcp_get_code":{"code_sha256":"fce10771ab1d00db"}},{"arxiv_id":"Chen_Each_Test_Image_Deserves_A_Specific_Prompt_Continual_Test-Time_Adaptation_CVPR_2024_paper","paper":null,"title":"arXiv:Chen_Each_Test_Image_Deserves_A_Specific_Prompt_Continual_Test-Time_Adaptation_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Chen-Ziyang/VPTTA","path":"OPTIC/networks/resnet.py","file_url":"https://github.com/Chen-Ziyang/VPTTA/blob/HEAD/OPTIC/networks/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Chen_CA-Jaccard_Camera-aware_Jaccard_Distance_for_Person_Re-identification_CVPR_2024_paper","paper":null,"title":"arXiv:Chen_CA-Jaccard_Camera-aware_Jaccard_Distance_for_Person_Re-identification_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chen960/CA-Jaccard","path":"caj/models/resnet_ibn_a.py","file_url":"https://github.com/chen960/CA-Jaccard/blob/HEAD/caj/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","paper":null,"title":"arXiv:Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SudongCAI/IIEU","path":"MODELS/resnet_elu_timm.py","file_url":"https://github.com/SudongCAI/IIEU/blob/HEAD/MODELS/resnet_elu_timm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"Apolinario_CODE-CL_Conceptor-Based_Gradient_Projection_for_Deep_Continual_Learning_ICCV_2025_paper","paper":null,"title":"arXiv:Apolinario_CODE-CL_Conceptor-Based_Gradient_Projection_for_Deep_Continual_Learning_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mapolinario94/CODE-CL","path":"models/nn_models/resnet18.py","file_url":"https://github.com/mapolinario94/CODE-CL/blob/HEAD/models/nn_models/resnet18.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4233fcdda81a8cb","mcp_get_code":{"code_sha256":"b4233fcdda81a8cb"}},{"arxiv_id":"2024.naacl-long.450","paper":null,"title":"arXiv:2024.naacl-long.450","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"microsoft/DeepSpeedExamples","path":"compression/cifar/resnet.py","file_url":"https://github.com/microsoft/DeepSpeedExamples/blob/HEAD/compression/cifar/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"2024.findings-emnlp.965","paper":null,"title":"arXiv:2024.findings-emnlp.965","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Chauncey-Jheng/PCRL-MRG","path":"llama_recipes/models/PCRL_llama/modeling_PCRL_llama.py","file_url":"https://github.com/Chauncey-Jheng/PCRL-MRG/blob/HEAD/llama_recipes/models/PCRL_llama/modeling_PCRL_llama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8f437ded506e6472","mcp_get_code":{"code_sha256":"8f437ded506e6472"}},{"arxiv_id":"2023.acl-long.803","paper":null,"title":"arXiv:2023.acl-long.803","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Shwai-He/PAD-Net","path":"dyconv/model/resnet_dcd_pad.py","file_url":"https://github.com/Shwai-He/PAD-Net/blob/HEAD/dyconv/model/resnet_dcd_pad.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"136970483","paper":null,"title":"arXiv:136970483","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"exped1230/S2-VER","path":"models/nets/resnet50.py","file_url":"https://github.com/exped1230/S2-VER/blob/HEAD/models/nets/resnet50.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"600ff2c45e0de056","mcp_get_code":{"code_sha256":"600ff2c45e0de056"}},{"arxiv_id":"136940019","paper":null,"title":"arXiv:136940019","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ETHRuiGong/TADA","path":"model/deeplabv2.py","file_url":"https://github.com/ETHRuiGong/TADA/blob/HEAD/model/deeplabv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"136910021","paper":null,"title":"arXiv:136910021","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"UCDvision/CMSF","path":"models/dense_resnet.py","file_url":"https://github.com/UCDvision/CMSF/blob/HEAD/models/dense_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"136890323","paper":null,"title":"arXiv:136890323","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KAIST-vilab/AEFT","path":"networks/resnet101.py","file_url":"https://github.com/KAIST-vilab/AEFT/blob/HEAD/networks/resnet101.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"136850512","paper":null,"title":"arXiv:136850512","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Impression2805/FMFP","path":"model/resnet.py","file_url":"https://github.com/Impression2805/FMFP/blob/HEAD/model/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6af95ebe99af2e36","mcp_get_code":{"code_sha256":"6af95ebe99af2e36"}},{"arxiv_id":"136850478","paper":null,"title":"arXiv:136850478","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"leo-gb/UMA","path":"ccs_training/models/moex_resnet.py","file_url":"https://github.com/leo-gb/UMA/blob/HEAD/ccs_training/models/moex_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"136830497","paper":null,"title":"arXiv:136830497","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VLOGroup/LVVCP","path":"common_utils/feat_match/resnet.py","file_url":"https://github.com/VLOGroup/LVVCP/blob/HEAD/common_utils/feat_match/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"136810551","paper":null,"title":"arXiv:136810551","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YtongXie/UniMiSS-code","path":"UniMiSS/models/Pacth_embeds.py","file_url":"https://github.com/YtongXie/UniMiSS-code/blob/HEAD/UniMiSS/models/Pacth_embeds.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5146b8c07ddcfc73","mcp_get_code":{"code_sha256":"5146b8c07ddcfc73"}},{"arxiv_id":"136740192","paper":null,"title":"arXiv:136740192","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CASIA-IVA-Lab/PASS-reID","path":"PASS_cluster_contrast_reid/clustercontrast/models/resnet_ibn_a.py","file_url":"https://github.com/CASIA-IVA-Lab/PASS-reID/blob/HEAD/PASS_cluster_contrast_reid/clustercontrast/models/resnet_ibn_a.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"136720346","paper":null,"title":"arXiv:136720346","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"gmum/ProtoPool","path":"resnet_features.py","file_url":"https://github.com/gmum/ProtoPool/blob/HEAD/resnet_features.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"136670659","paper":null,"title":"arXiv:136670659","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"PardoAlejo/MovieCuts","path":"models/resnet.py","file_url":"https://github.com/PardoAlejo/MovieCuts/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"160bb14bd76201b4","mcp_get_code":{"code_sha256":"160bb14bd76201b4"}},{"arxiv_id":"136640628","paper":null,"title":"arXiv:136640628","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"JianhongPan/GradAuto","path":"skipnet/imagenet/models.py","file_url":"https://github.com/JianhongPan/GradAuto/blob/HEAD/skipnet/imagenet/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"04824","paper":null,"title":"arXiv:04824","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"asw91666/TRG-Release","path":"models/trg_model/pose_resnet.py","file_url":"https://github.com/asw91666/TRG-Release/blob/HEAD/models/trg_model/pose_resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}},{"arxiv_id":"03451","paper":null,"title":"arXiv:03451","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zhongshsh/ASR","path":"models/resnet.py","file_url":"https://github.com/zhongshsh/ASR/blob/HEAD/models/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fac5364e2f53c6db","mcp_get_code":{"code_sha256":"fac5364e2f53c6db"}}]}