{"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/conv-block","entry":"conv_block","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":95,"n_papers_ran":48,"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":105,"n_samples_ran":50,"n_samples_fingerprinted":13,"n_places":128,"n_places_pointer_only":45,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":18,"ran_fixture":8,"ran":24,"unverified":55},"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":"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/PBM","path":"mrcnn/model.py","file_url":"https://github.com/CVRL/PBM/blob/HEAD/mrcnn/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"719785e60bebf517","mcp_get_code":{"code_sha256":"719785e60bebf517"}},{"arxiv_id":"2604.22056","paper":"/paper/arxiv-2604-22056","title":"Learning Coverage-and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"CagkanYapar/Deployment1Tx","path":"models/diffusion.py","file_url":"https://github.com/CagkanYapar/Deployment1Tx/blob/HEAD/models/diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89f84f0bf94f04b5","mcp_get_code":{"code_sha256":"89f84f0bf94f04b5"}},{"arxiv_id":"2604.22056","paper":"/paper/arxiv-2604-22056","title":"Learning Coverage-and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"CagkanYapar/Deployment1Tx","path":"models/discriminative.py","file_url":"https://github.com/CagkanYapar/Deployment1Tx/blob/HEAD/models/discriminative.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76d16d27813c48f1","mcp_get_code":{"code_sha256":"76d16d27813c48f1"}},{"arxiv_id":"2407.15420","paper":"/paper/local-all-pair-correspondence-for-point","title":"Local All-Pair Correspondence for Point Tracking","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-kaist/locotrack","path":"locotrack/models/cmdtop.py","file_url":"https://github.com/cvlab-kaist/locotrack/blob/HEAD/locotrack/models/cmdtop.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":"a8329e1fb8a56308","mcp_get_code":{"code_sha256":"a8329e1fb8a56308"}},{"arxiv_id":"2406.04284","paper":"/paper/what-is-dataset-distillation-learning","title":"What is Dataset Distillation Learning?","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princetonvisualai/What-is-Dataset-Distillation-Learning","path":"networks.py","file_url":"https://github.com/princetonvisualai/What-is-Dataset-Distillation-Learning/blob/HEAD/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f0905c1fdd06eba0","mcp_get_code":{"code_sha256":"f0905c1fdd06eba0"}},{"arxiv_id":"2405.08958","paper":"/paper/learned-radio-interferometric-imaging-for","title":"Learned radio interferometric imaging for varying visibility coverage","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"astro-informatics/leia","path":"src/network.py","file_url":"https://github.com/astro-informatics/leia/blob/HEAD/src/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5289b06027cb3e97","mcp_get_code":{"code_sha256":"5289b06027cb3e97"}},{"arxiv_id":"2404.15700","paper":"/paper/mas-sam-segment-any-marine-animal-with","title":"MAS-SAM: Segment Any Marine Animal with Aggregated Features","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Drchip61/MAS-SAM","path":"MAS-SAM/sam_lora_image_encoder.py","file_url":"https://github.com/Drchip61/MAS-SAM/blob/HEAD/MAS-SAM/sam_lora_image_encoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"761a072ba4760197","mcp_get_code":{"code_sha256":"761a072ba4760197"}},{"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/conv_imagenet.py","file_url":"https://github.com/byyx666/archcraft/blob/HEAD/class_il/convs/conv_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":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2404.11265","paper":"/paper/the-victim-and-the-beneficiary-exploiting-a-1","title":"The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data","date":"2024-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zixuan-zhu/vab","path":"models/Conv4.py","file_url":"https://github.com/zixuan-zhu/vab/blob/HEAD/models/Conv4.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"15fbe2fdbc1b71ce","mcp_get_code":{"code_sha256":"15fbe2fdbc1b71ce"}},{"arxiv_id":"2404.10824","paper":"/paper/decoupled-weight-decay-for-any-p-norm","title":"Decoupled Weight Decay for Any $p$ Norm","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nadav-out/padam","path":"python/.ipynb_checkpoints/models-checkpoint.py","file_url":"https://github.com/nadav-out/padam/blob/HEAD/python/.ipynb_checkpoints/models-checkpoint.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0a35cfc2ffcf35f9","mcp_get_code":{"code_sha256":"0a35cfc2ffcf35f9"}},{"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/conv_imagenet.py","file_url":"https://github.com/wannaa/c-flat/blob/HEAD/convs/conv_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":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2403.06674","paper":"/paper/car-damage-detection-and-patch-to-patch-self","title":"Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2000222/car-damage-detectionv0","path":"mrcnn/model.py","file_url":"https://github.com/2000222/car-damage-detectionv0/blob/HEAD/mrcnn/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"719785e60bebf517","mcp_get_code":{"code_sha256":"719785e60bebf517"}},{"arxiv_id":"2402.03749","paper":"/paper/vision-superalignment-weak-to-strong","title":"Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggjy/vision_weak_to_strong","path":"few_shot_leaerning/models/convnet4.py","file_url":"https://github.com/ggjy/vision_weak_to_strong/blob/HEAD/few_shot_leaerning/models/convnet4.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":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2402.00712","paper":"/paper/chaosbench-a-multi-channel-physics-based","title":"ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leap-stc/ChaosBench","path":"chaosbench/models/cnn.py","file_url":"https://github.com/leap-stc/ChaosBench/blob/HEAD/chaosbench/models/cnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"143f63c99701b6a9","mcp_get_code":{"code_sha256":"143f63c99701b6a9"}},{"arxiv_id":"2309.06086","paper":"/paper/plasticity-optimized-complementary-networks","title":"Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning","date":"2023-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alviur/pocon_wacv2024","path":"src/approach/FT_online.py","file_url":"https://github.com/alviur/pocon_wacv2024/blob/HEAD/src/approach/FT_online.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e2a5fdc0a059abac","mcp_get_code":{"code_sha256":"e2a5fdc0a059abac"}},{"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":"few-shot-learning-on-chest-x-ray/fsl_subspace","path":"models/ResNet12_embedding.py","file_url":"https://github.com/few-shot-learning-on-chest-x-ray/fsl_subspace/blob/HEAD/models/ResNet12_embedding.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":"4018d80d0425da98","mcp_get_code":{"code_sha256":"4018d80d0425da98"}},{"arxiv_id":"2307.04341","paper":"/paper/stroke-extraction-of-chinese-character-based","title":"Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration","date":"2023-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MengLi-l1/StrokeExtraction","path":"model/model_of_SDNet.py","file_url":"https://github.com/MengLi-l1/StrokeExtraction/blob/HEAD/model/model_of_SDNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"260713377ec930c1","mcp_get_code":{"code_sha256":"260713377ec930c1"}},{"arxiv_id":"2306.06766","paper":"/paper/generalizable-wireless-navigation-through","title":"Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning","date":"2023-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"panshark/pirl-win","path":"function_approximator/deep.py","file_url":"https://github.com/panshark/pirl-win/blob/HEAD/function_approximator/deep.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa704557f206e456","mcp_get_code":{"code_sha256":"aa704557f206e456"}},{"arxiv_id":"2305.02618","paper":"/paper/semantic-aware-generation-of-multi-view","title":"Semantic-aware Generation of Multi-view Portrait Drawings","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AiArt-HDU/SAGE","path":"generators/refinegan.py","file_url":"https://github.com/AiArt-HDU/SAGE/blob/HEAD/generators/refinegan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"265de8ce99feec5b","mcp_get_code":{"code_sha256":"265de8ce99feec5b"}},{"arxiv_id":"2304.06976","paper":"/paper/bitstream-corrupted-jpeg-images-are","title":"Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenyang001/Two-ACIR","path":"Recovery network/models/refined_guiding.py","file_url":"https://github.com/wenyang001/Two-ACIR/blob/HEAD/Recovery%20network/models/refined_guiding.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4c547a7bb20185e","mcp_get_code":{"code_sha256":"e4c547a7bb20185e"}},{"arxiv_id":"2303.08010","paper":"/paper/window-based-early-exit-cascades-for","title":"Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models","date":"2023-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keras-team/keras","path":"keras/src/applications/densenet.py","file_url":"https://github.com/keras-team/keras/blob/HEAD/keras/src/applications/densenet.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":"0dd0e7197a272037","mcp_get_code":{"code_sha256":"0dd0e7197a272037"}},{"arxiv_id":"2212.14041","paper":"/paper/rfold-towards-simple-yet-effective-rna","title":"Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective","date":"2022-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"a4bio/rfold","path":"rfold.py","file_url":"https://github.com/a4bio/rfold/blob/HEAD/rfold.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8db3b7816ada110","mcp_get_code":{"code_sha256":"c8db3b7816ada110"}},{"arxiv_id":"2212.10305","paper":"/paper/which-pixel-to-annotate-a-label-efficient","title":"Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework","date":"2022-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lhaof/nuseg","path":"pseudo_label/mrcnn/model.py","file_url":"https://github.com/lhaof/nuseg/blob/HEAD/pseudo_label/mrcnn/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b01bd9e893f43e5","mcp_get_code":{"code_sha256":"8b01bd9e893f43e5"}},{"arxiv_id":"2210.03209","paper":"/paper/self-adaptive-driving-in-nonstationary","title":"Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation","date":null,"month_inferred_from_arxiv_id":"2022-10","title_source":"archive","repo":"panshark/cola","path":"function_approximator/deep.py","file_url":"https://github.com/panshark/cola/blob/HEAD/function_approximator/deep.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa704557f206e456","mcp_get_code":{"code_sha256":"aa704557f206e456"}},{"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/conv4.py","file_url":"https://github.com/cser-tang-hao/bsfa-fsfg/blob/HEAD/models/conv4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2203.05483","paper":"/paper/projunn-efficient-method-for-training-deep","title":"projUNN: efficient method for training deep networks with unitary matrices","date":"2022-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/projunn","path":"projunn/models.py","file_url":"https://github.com/facebookresearch/projunn/blob/HEAD/projunn/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"cd72b1bb6792152e","mcp_get_code":{"code_sha256":"cd72b1bb6792152e"}},{"arxiv_id":"2201.10055","paper":"/paper/identifying-a-training-set-attack-s-target","title":"Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation","date":"2022-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zaydh/target_identification","path":"fig01_cifar_vs_mnist/poison/datasets/_cifar10_resnet.py","file_url":"https://github.com/zaydh/target_identification/blob/HEAD/fig01_cifar_vs_mnist/poison/datasets/_cifar10_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fad1b2793ea50e0","mcp_get_code":{"code_sha256":"9fad1b2793ea50e0"}},{"arxiv_id":"2112.15362","paper":"/paper/calibrated-hyperspectral-image-reconstruction","title":"Modeling Mask Uncertainty in Hyperspectral Image Reconstruction","date":"2021-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiamian-wang/mask_uncertainty_spectral_sci","path":"network/GST.py","file_url":"https://github.com/jiamian-wang/mask_uncertainty_spectral_sci/blob/HEAD/network/GST.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":"f6ab834d147ab456","mcp_get_code":{"code_sha256":"f6ab834d147ab456"}},{"arxiv_id":"2112.02447","paper":"/paper/next-day-wildfire-spread-a-machine-learning","title":"Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data","date":"2021-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satellitevu/satellitevu-aws-disaster-response-hackathon","path":"deep_learning/model_resunet.py","file_url":"https://github.com/satellitevu/satellitevu-aws-disaster-response-hackathon/blob/HEAD/deep_learning/model_resunet.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":"9ebda9e9a9e6bed7","mcp_get_code":{"code_sha256":"9ebda9e9a9e6bed7"}},{"arxiv_id":"2105.10398","paper":"/paper/semi-supervised-learning-for-identifying-the","title":"Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia","date":"2021-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RoonakR/Agitation_detection","path":"model/model.py","file_url":"https://github.com/RoonakR/Agitation_detection/blob/HEAD/model/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"533809aab30a27fd","mcp_get_code":{"code_sha256":"533809aab30a27fd"}},{"arxiv_id":"2104.09556","paper":"/paper/removing-diffraction-image-artifacts-in-under","title":"Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jnjaby/DISCNet","path":"basicsr/models/archs/discnet_arch.py","file_url":"https://github.com/jnjaby/DISCNet/blob/HEAD/basicsr/models/archs/discnet_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":"2088da310e2c1c79","mcp_get_code":{"code_sha256":"2088da310e2c1c79"}},{"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/convnet.py","file_url":"https://github.com/njulus/ST/blob/HEAD/networks/convnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2104.01677","paper":"/paper/a-contrastive-rule-for-meta-learning","title":"A contrastive rule for meta-learning","date":"2021-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smonsays/contrastive-meta-learning","path":"fewshot/model.py","file_url":"https://github.com/smonsays/contrastive-meta-learning/blob/HEAD/fewshot/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ed0b510614476bf6","mcp_get_code":{"code_sha256":"ed0b510614476bf6"}},{"arxiv_id":"2103.16483","paper":"/paper/benchmarking-representation-learning-for","title":"Benchmarking Representation Learning for Natural World Image Collections","date":"2021-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visipedia/newt","path":"benchmark/tf_resnet.py","file_url":"https://github.com/visipedia/newt/blob/HEAD/benchmark/tf_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbe50e0989ae1e3f","mcp_get_code":{"code_sha256":"cbe50e0989ae1e3f"}},{"arxiv_id":"2101.03149","paper":"/paper/visualvoice-audio-visual-speech-separation","title":"VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency","date":"2021-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/visualvoice","path":"models/networks.py","file_url":"https://github.com/facebookresearch/visualvoice/blob/HEAD/models/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"123ba7dfc88acf63","mcp_get_code":{"code_sha256":"123ba7dfc88acf63"}},{"arxiv_id":"2011.06294","paper":"/paper/rife-real-time-intermediate-flow-estimation","title":"RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation","date":"2020-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amogh7joshi/media-vision","path":"mediavision/interpolate/rife/model.py","file_url":"https://github.com/amogh7joshi/media-vision/blob/HEAD/mediavision/interpolate/rife/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"171240f337b5ff24","mcp_get_code":{"code_sha256":"171240f337b5ff24"}},{"arxiv_id":"2010.14406","paper":"/paper/transporter-networks-rearranging-the-visual","title":"Transporter Networks: Rearranging the Visual World for Robotic Manipulation","date":null,"month_inferred_from_arxiv_id":"2020-10","title_source":"archive","repo":"google-research/ravens","path":"ravens/models/resnet.py","file_url":"https://github.com/google-research/ravens/blob/HEAD/ravens/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":"a9fb72a7d344bddd","mcp_get_code":{"code_sha256":"a9fb72a7d344bddd"}},{"arxiv_id":"2010.06498","paper":"/paper/cross-domain-few-shot-learning-by-1","title":"Cross-Domain Few-Shot Learning by Representation Fusion","date":"2020-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/chef","path":"models.py","file_url":"https://github.com/ml-jku/chef/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21ffa3647b971a8f","mcp_get_code":{"code_sha256":"21ffa3647b971a8f"}},{"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/convnet4.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/convnet4.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":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"2009.07641","paper":"/paper/bsn-complementary-boundary-regressor-with","title":"BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation","date":"2020-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xxcheng0708/BSNPlusPlus-boundary-sensitive-network","path":"models.py","file_url":"https://github.com/xxcheng0708/BSNPlusPlus-boundary-sensitive-network/blob/HEAD/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83a6fac7349fd1f0","mcp_get_code":{"code_sha256":"83a6fac7349fd1f0"}},{"arxiv_id":"2008.11297","paper":"/paper/transductive-information-maximization-for-few","title":"Transductive Information Maximization For Few-Shot Learning","date":"2020-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mboudiaf/TIM","path":"src/models/Conv4.py","file_url":"https://github.com/mboudiaf/TIM/blob/HEAD/src/models/Conv4.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"02652b1ac1a54dda","mcp_get_code":{"code_sha256":"02652b1ac1a54dda"}},{"arxiv_id":"2006.07500","paper":"/paper/domain-generalization-using-causal-matching-1","title":"Domain Generalization using Causal Matching","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biomedia-mira/masf","path":"masf_func.py","file_url":"https://github.com/biomedia-mira/masf/blob/HEAD/masf_func.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d7335824a1d78b44","mcp_get_code":{"code_sha256":"d7335824a1d78b44"}},{"arxiv_id":"2006.03829","paper":"/paper/3d-self-supervised-methods-for-medical","title":"3D Self-Supervised Methods for Medical Imaging","date":"2020-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HealthML/self-supervised-3d-tasks","path":"self_supervised_3d_tasks/models/unet.py","file_url":"https://github.com/HealthML/self-supervised-3d-tasks/blob/HEAD/self_supervised_3d_tasks/models/unet.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":"40baa5aebd753951","mcp_get_code":{"code_sha256":"40baa5aebd753951"}},{"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":"adahessian_tf/Models/VGGs.py","file_url":"https://github.com/amirgholami/adahessian/blob/HEAD/adahessian_tf/Models/VGGs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6290891118db623e","mcp_get_code":{"code_sha256":"6290891118db623e"}},{"arxiv_id":"2004.08790","paper":"/paper/unet-3-a-full-scale-connected-unet-for","title":"UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation","date":"2020-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hamidriasat/UNet-3-Plus","path":"models/unet3plus_utils.py","file_url":"https://github.com/hamidriasat/UNet-3-Plus/blob/HEAD/models/unet3plus_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc407cea8c628ee7","mcp_get_code":{"code_sha256":"dc407cea8c628ee7"}},{"arxiv_id":"2003.06975","paper":"/paper/taco-trash-annotations-in-context-for-litter","title":"TACO: Trash Annotations in Context for Litter Detection","date":"2020-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pedropro/TACO","path":"detector/model.py","file_url":"https://github.com/pedropro/TACO/blob/HEAD/detector/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"918c5de88a445168","mcp_get_code":{"code_sha256":"918c5de88a445168"}},{"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/convnet4.py","file_url":"https://github.com/cyvius96/few-shot-meta-baseline/blob/HEAD/meta-dataset/models/convnet4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"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":"sayakpaul/Denoised-Smoothing-TF","path":"models/dncnn.py","file_url":"https://github.com/sayakpaul/Denoised-Smoothing-TF/blob/HEAD/models/dncnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9650305844ec10e","mcp_get_code":{"code_sha256":"b9650305844ec10e"}},{"arxiv_id":"2002.09576","paper":"/paper/unmask-adversarial-detection-and-defense","title":"UnMask: Adversarial Detection and Defense Through Robust Feature Alignment","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"safreita1/unmask","path":"Mask_RCNN/mrcnn/model.py","file_url":"https://github.com/safreita1/unmask/blob/HEAD/Mask_RCNN/mrcnn/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"719785e60bebf517","mcp_get_code":{"code_sha256":"719785e60bebf517"}},{"arxiv_id":"2002.08484","paper":"/paper/estimating-training-data-influence-by","title":"Estimating Training Data Influence by Tracing Gradient Descent","date":"2020-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frederick0329/TracIn","path":"imagenet/resnet50/resnet.py","file_url":"https://github.com/frederick0329/TracIn/blob/HEAD/imagenet/resnet50/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":"6418d1ea781bab93","mcp_get_code":{"code_sha256":"6418d1ea781bab93"}},{"arxiv_id":"1912.08193","paper":"/paper/pointrend-image-segmentation-as-rendering","title":"PointRend: Image Segmentation as Rendering","date":"2019-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayoolaolafenwa/PixelLib","path":"pixellib/instance/mask_rcnn.py","file_url":"https://github.com/ayoolaolafenwa/PixelLib/blob/HEAD/pixellib/instance/mask_rcnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c9b8c863cf414f7","mcp_get_code":{"code_sha256":"0c9b8c863cf414f7"}},{"arxiv_id":"1912.07160","paper":"/paper/damagenet-a-universal-adversarial-dataset","title":"DAmageNet: A Universal Adversarial Dataset","date":"2019-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XingLiangLondon/Image-Similarity-in-Percentage","path":"resnet50.py","file_url":"https://github.com/XingLiangLondon/Image-Similarity-in-Percentage/blob/HEAD/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e7a557c615eb17e","mcp_get_code":{"code_sha256":"5e7a557c615eb17e"}},{"arxiv_id":"1911.06045","paper":"/paper/self-supervised-learning-for-few-shot-image","title":"Self-Supervised Learning For Few-Shot Image Classification","date":"2019-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phecy/SSL-FEW-SHOT","path":"feat/networks/convnet.py","file_url":"https://github.com/phecy/SSL-FEW-SHOT/blob/HEAD/feat/networks/convnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"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/Conv4.py","file_url":"https://github.com/mileyan/simple_shot/blob/HEAD/src/models/Conv4.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"02652b1ac1a54dda","mcp_get_code":{"code_sha256":"02652b1ac1a54dda"}},{"arxiv_id":"1908.07433","paper":"/paper/pix2pose-pixel-wise-coordinate-regression-of","title":"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation","date":"2019-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kirumang/Pix2Pose","path":"pix2pose_model/resnet50_mod.py","file_url":"https://github.com/kirumang/Pix2Pose/blob/HEAD/pix2pose_model/resnet50_mod.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c20710ab8a2abc9","mcp_get_code":{"code_sha256":"0c20710ab8a2abc9"}},{"arxiv_id":"1908.05257","paper":"/paper/few-shot-learning-with-global-class","title":"Few-Shot Learning with Global Class Representations","date":"2019-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tiangeluo/fsl-global","path":"convnet.py","file_url":"https://github.com/tiangeluo/fsl-global/blob/HEAD/convnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-2.0","inline_ok":false,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"1906.11129","paper":"/paper/uncertainty-guided-multi-scale-residual-1","title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeevyasarla/UMRL--using-Cycle-Spinning","path":"models/derain_mulcmp.py","file_url":"https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning/blob/HEAD/models/derain_mulcmp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0da6b359cfe07ea0","mcp_get_code":{"code_sha256":"0da6b359cfe07ea0"}},{"arxiv_id":"1905.04172","paper":"/paper/on-the-connection-between-adversarial","title":"On the Connection Between Adversarial Robustness and Saliency Map Interpretability","date":"2019-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cetmann/robustness-interpretability","path":"resnet50.py","file_url":"https://github.com/cetmann/robustness-interpretability/blob/HEAD/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ceb111da64aa651","mcp_get_code":{"code_sha256":"8ceb111da64aa651"}},{"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":"bubbliiiing/retinaface-keras","path":"nets/resnet.py","file_url":"https://github.com/bubbliiiing/retinaface-keras/blob/HEAD/nets/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4b2f767b1cfc5ed","mcp_get_code":{"code_sha256":"b4b2f767b1cfc5ed"}},{"arxiv_id":"1904.01569","paper":"/paper/exploring-randomly-wired-neural-networks-for","title":"Exploring Randomly Wired Neural Networks for Image Recognition","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"swdsld/RandWire_tensorflow","path":"network/RandWire.py","file_url":"https://github.com/swdsld/RandWire_tensorflow/blob/HEAD/network/RandWire.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"925c72f289b0471c","mcp_get_code":{"code_sha256":"925c72f289b0471c"}},{"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":"srihari-humbarwadi/tensorflow_fcos","path":"tensorflow_fcos/models/blocks.py","file_url":"https://github.com/srihari-humbarwadi/tensorflow_fcos/blob/HEAD/tensorflow_fcos/models/blocks.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":"43351463fea5b39a","mcp_get_code":{"code_sha256":"43351463fea5b39a"}},{"arxiv_id":"1902.10107","paper":"/paper/utterance-level-aggregation-for-speaker","title":"Utterance-level Aggregation For Speaker Recognition In The Wild","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"khassanoff/Speaker_Verification","path":"speech_embedder_net.py","file_url":"https://github.com/khassanoff/Speaker_Verification/blob/HEAD/speech_embedder_net.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"17437f2725b26ae5","mcp_get_code":{"code_sha256":"17437f2725b26ae5"}},{"arxiv_id":"1902.06918","paper":"/paper/explaining-a-black-box-using-deep-variational","title":"Explaining a black-box using Deep Variational Information Bottleneck Approach","date":"2019-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"willisk/VIBI","path":"models.py","file_url":"https://github.com/willisk/VIBI/blob/HEAD/models.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":"1b6f041604f5ceb5","mcp_get_code":{"code_sha256":"1b6f041604f5ceb5"}},{"arxiv_id":"1902.04502","paper":"/paper/fast-scnn-fast-semantic-segmentation-network","title":"Fast-SCNN: Fast Semantic Segmentation Network","date":"2019-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbaofd/solar-panels-detection","path":"model_list/fast_scnn_0.py","file_url":"https://github.com/dbaofd/solar-panels-detection/blob/HEAD/model_list/fast_scnn_0.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"721b77a7a5de2adf","mcp_get_code":{"code_sha256":"721b77a7a5de2adf"}},{"arxiv_id":"1902.04502","paper":"/paper/fast-scnn-fast-semantic-segmentation-network","title":"Fast-SCNN: Fast Semantic Segmentation Network","date":"2019-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbaofd/solar-panels-detection","path":"model_list/segnet_3.py","file_url":"https://github.com/dbaofd/solar-panels-detection/blob/HEAD/model_list/segnet_3.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"740cb4ae8f653627","mcp_get_code":{"code_sha256":"740cb4ae8f653627"}},{"arxiv_id":"1812.07032","paper":"/paper/boundary-loss-for-highly-unbalanced","title":"Boundary loss for highly unbalanced segmentation","date":"2018-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LIVIAETS/boundary-loss","path":"models/residualunet.py","file_url":"https://github.com/LIVIAETS/boundary-loss/blob/HEAD/models/residualunet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b44139feb813153e","mcp_get_code":{"code_sha256":"b44139feb813153e"}},{"arxiv_id":"1811.11283","paper":"/paper/a-compact-embedding-for-facial-expression","title":"A Compact Embedding for Facial Expression Similarity","date":"2018-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GerardLiu96/FECNet","path":"FEC.py","file_url":"https://github.com/GerardLiu96/FECNet/blob/HEAD/FEC.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"729792267d24f02c","mcp_get_code":{"code_sha256":"729792267d24f02c"}},{"arxiv_id":"1811.11283","paper":"/paper/a-compact-embedding-for-facial-expression","title":"A Compact Embedding for Facial Expression Similarity","date":"2018-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GerardLiu96/FECNet","path":"FECWithPretrained.py","file_url":"https://github.com/GerardLiu96/FECNet/blob/HEAD/FECWithPretrained.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d016d07154115e5b","mcp_get_code":{"code_sha256":"d016d07154115e5b"}},{"arxiv_id":"1808.02473","paper":"/paper/sketchyscene-richly-annotated-scene-sketches","title":"SketchyScene: Richly-Annotated Scene Sketches","date":"2018-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SketchyScene/SketchyScene","path":"Instance_Segmentation/libs/model.py","file_url":"https://github.com/SketchyScene/SketchyScene/blob/HEAD/Instance_Segmentation/libs/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f998b7fc598210b3","mcp_get_code":{"code_sha256":"f998b7fc598210b3"}},{"arxiv_id":"1806.07823","paper":"/paper/unsupervised-learning-of-object-landmarks","title":"Unsupervised Learning of Object Landmarks through Conditional Image Generation","date":"2018-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hqng/imm-pytorch","path":"imm_model.py","file_url":"https://github.com/hqng/imm-pytorch/blob/HEAD/imm_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":"c0e78e44c61de61a","mcp_get_code":{"code_sha256":"c0e78e44c61de61a"}},{"arxiv_id":"1806.03287","paper":"/paper/slalom-fast-verifiable-and-private-execution","title":"Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware","date":"2018-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ftramer/slalom","path":"python/slalom/resnet.py","file_url":"https://github.com/ftramer/slalom/blob/HEAD/python/slalom/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f8b213998b7f612","mcp_get_code":{"code_sha256":"1f8b213998b7f612"}},{"arxiv_id":"1804.06812","paper":"/paper/ecg-arrhythmia-classification-using-a-2-d","title":"ECG arrhythmia classification using a 2-D convolutional neural network","date":"2018-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lxdv/ecg-classification","path":"models/models1d.py","file_url":"https://github.com/lxdv/ecg-classification/blob/HEAD/models/models1d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8459804c5f269226","mcp_get_code":{"code_sha256":"8459804c5f269226"}},{"arxiv_id":"1804.02967","paper":"/paper/hyperdense-net-a-hyper-densely-connected-cnn","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","date":"2018-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"black0017/MedicalZooPytorch","path":"lib/medzoo/HyperDensenet.py","file_url":"https://github.com/black0017/MedicalZooPytorch/blob/HEAD/lib/medzoo/HyperDensenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b44139feb813153e","mcp_get_code":{"code_sha256":"b44139feb813153e"}},{"arxiv_id":"1804.02767","paper":"/paper/yolov3-an-incremental-improvement","title":"YOLOv3: An Incremental Improvement","date":"2018-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DevBruce/YOLOv3-TF2","path":"libs/models/layers.py","file_url":"https://github.com/DevBruce/YOLOv3-TF2/blob/HEAD/libs/models/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"67f851a5d61759bc","mcp_get_code":{"code_sha256":"67f851a5d61759bc"}},{"arxiv_id":"1802.09232","paper":"/paper/2d3d-pose-estimation-and-action-recognition","title":"2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning","date":"2018-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dluvizon/deephar","path":"deephar/models/blocks.py","file_url":"https://github.com/dluvizon/deephar/blob/HEAD/deephar/models/blocks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd1d092a82873639","mcp_get_code":{"code_sha256":"dd1d092a82873639"}},{"arxiv_id":"1704.04861","paper":"/paper/mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sek788432/Intel-Scene-Image-Classification","path":"my_mobilenet.py","file_url":"https://github.com/sek788432/Intel-Scene-Image-Classification/blob/HEAD/my_mobilenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"279b7b5807cb64e1","mcp_get_code":{"code_sha256":"279b7b5807cb64e1"}},{"arxiv_id":"1703.06870","paper":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AKASH2907/bird-species-classification","path":"mask_rcnn/mrcnn/model.py","file_url":"https://github.com/AKASH2907/bird-species-classification/blob/HEAD/mask_rcnn/mrcnn/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"719785e60bebf517","mcp_get_code":{"code_sha256":"719785e60bebf517"}},{"arxiv_id":"1703.06870","paper":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waspinator/deep-learning-explorer","path":"mask-rcnn/libraries/mrcnn/model.py","file_url":"https://github.com/waspinator/deep-learning-explorer/blob/HEAD/mask-rcnn/libraries/mrcnn/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":"918c5de88a445168","mcp_get_code":{"code_sha256":"918c5de88a445168"}},{"arxiv_id":"1703.06870","paper":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itsasimiqbal/SeBRe","path":"model.py","file_url":"https://github.com/itsasimiqbal/SeBRe/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":"f998b7fc598210b3","mcp_get_code":{"code_sha256":"f998b7fc598210b3"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","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":"4018d80d0425da98","mcp_get_code":{"code_sha256":"4018d80d0425da98"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jsalbert/prototypical-networks","path":"models/convnet_mini.py","file_url":"https://github.com/jsalbert/prototypical-networks/blob/HEAD/models/convnet_mini.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d556d61294bb0a8","mcp_get_code":{"code_sha256":"9d556d61294bb0a8"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyvius96/prototypical-network-pytorch","path":"convnet.py","file_url":"https://github.com/cyvius96/prototypical-network-pytorch/blob/HEAD/convnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0a1286b15398404","mcp_get_code":{"code_sha256":"b0a1286b15398404"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DrMMZ/ProtoNet","path":"model/ProtoNet.py","file_url":"https://github.com/DrMMZ/ProtoNet/blob/HEAD/model/ProtoNet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf404583e5ea3465","mcp_get_code":{"code_sha256":"bf404583e5ea3465"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joshfp/one-shot-learning","path":"model.py","file_url":"https://github.com/joshfp/one-shot-learning/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"57d2c3e6cce78748","mcp_get_code":{"code_sha256":"57d2c3e6cce78748"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theneuralbeing/maml","path":"model.py","file_url":"https://github.com/theneuralbeing/maml/blob/HEAD/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":"0119024a5f2c09b7","mcp_get_code":{"code_sha256":"0119024a5f2c09b7"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lgcollins/tr-maml","path":"sinusoid/maml.py","file_url":"https://github.com/lgcollins/tr-maml/blob/HEAD/sinusoid/maml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cc6f21a12ec084fc","mcp_get_code":{"code_sha256":"cc6f21a12ec084fc"}},{"arxiv_id":"1702.01983","paper":"/paper/face-aging-with-conditional-generative","title":"Face Aging With Conditional Generative Adversarial Networks","date":"2017-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/GSoC-TensorFlow-2019","path":"mask_rcnn/model.py","file_url":"https://github.com/Vishal-V/GSoC-TensorFlow-2019/blob/HEAD/mask_rcnn/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":"4a91e0c12a4df0bc","mcp_get_code":{"code_sha256":"4a91e0c12a4df0bc"}},{"arxiv_id":"1612.03716","paper":"/paper/coco-stuff-thing-and-stuff-classes-in-context","title":"COCO-Stuff: Thing and Stuff Classes in Context","date":"2016-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"divyanshpuri02/COCO_2018-Stuff-Segmentation-Challenge","path":"COCO_2018-Stuff-Segmentation-Challenge/keras_segmentation/models/resnet50.py","file_url":"https://github.com/divyanshpuri02/COCO_2018-Stuff-Segmentation-Challenge/blob/HEAD/COCO_2018-Stuff-Segmentation-Challenge/keras_segmentation/models/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee7d77d25024c3a5","mcp_get_code":{"code_sha256":"ee7d77d25024c3a5"}},{"arxiv_id":"1612.03242","paper":"/paper/stackgan-text-to-photo-realistic-image","title":"StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks","date":"2016-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vishal-V/GSoC","path":"mask_rcnn/model.py","file_url":"https://github.com/Vishal-V/GSoC/blob/HEAD/mask_rcnn/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":"4a91e0c12a4df0bc","mcp_get_code":{"code_sha256":"4a91e0c12a4df0bc"}},{"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":"soumik12345/point-cloud-segmentation","path":"point_seg/pointnet/models.py","file_url":"https://github.com/soumik12345/point-cloud-segmentation/blob/HEAD/point_seg/pointnet/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a7d70d6b0134a2a","mcp_get_code":{"code_sha256":"2a7d70d6b0134a2a"}},{"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":"Sakib1263/ResNet1D-Model-Builder-KERAS","path":"Codes/ResNeXt_2DCNN.py","file_url":"https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS/blob/HEAD/Codes/ResNeXt_2DCNN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c280f69865379df5","mcp_get_code":{"code_sha256":"c280f69865379df5"}},{"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":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_1DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1c780b9dd4de663f","mcp_get_code":{"code_sha256":"1c780b9dd4de663f"}},{"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":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_2DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"367d94b9992d89f0","mcp_get_code":{"code_sha256":"367d94b9992d89f0"}},{"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":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_v2_1DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_v2_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cdf432637440f6a8","mcp_get_code":{"code_sha256":"cdf432637440f6a8"}},{"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":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_v2_2DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_v2_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"646da7216686f082","mcp_get_code":{"code_sha256":"646da7216686f082"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/DenseNet-1D-2D-Tensorflow-Keras","path":"Codes/DenseNet_1DCNN.py","file_url":"https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras/blob/HEAD/Codes/DenseNet_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a324e3ecd95d62e4","mcp_get_code":{"code_sha256":"a324e3ecd95d62e4"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/DenseNet-1D-2D-Tensorflow-Keras","path":"Codes/DenseNet_2DCNN.py","file_url":"https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras/blob/HEAD/Codes/DenseNet_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97627c7cc3973ed6","mcp_get_code":{"code_sha256":"97627c7cc3973ed6"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lionelmessi6410/ntga","path":"models/densenet.py","file_url":"https://github.com/lionelmessi6410/ntga/blob/HEAD/models/densenet.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":"4e3b1fedf80792c3","mcp_get_code":{"code_sha256":"4e3b1fedf80792c3"}},{"arxiv_id":"1606.06650","paper":"/paper/3d-u-net-learning-dense-volumetric","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","date":"2016-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenyizi086/wu.2023.sigspatial","path":"model/unet3d/unet3d.py","file_url":"https://github.com/chenyizi086/wu.2023.sigspatial/blob/HEAD/model/unet3d/unet3d.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":"aaac350375676434","mcp_get_code":{"code_sha256":"aaac350375676434"}},{"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":"ewinata/CIFAR-10","path":"wresnet20/cifar_10_wresnet20.py","file_url":"https://github.com/ewinata/CIFAR-10/blob/HEAD/wresnet20/cifar_10_wresnet20.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":"85623751f83e4466","mcp_get_code":{"code_sha256":"85623751f83e4466"}},{"arxiv_id":"1602.01783","paper":"/paper/asynchronous-methods-for-deep-reinforcement","title":"Asynchronous Methods for Deep Reinforcement Learning","date":"2016-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gungui98/deeprl-a3c-ai2thor","path":"utils/networks.py","file_url":"https://github.com/gungui98/deeprl-a3c-ai2thor/blob/HEAD/utils/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee54b4f2b5bd8b07","mcp_get_code":{"code_sha256":"ee54b4f2b5bd8b07"}},{"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":"Sangkwun/Resnet","path":"resnet.py","file_url":"https://github.com/Sangkwun/Resnet/blob/HEAD/resnet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"none","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b3e6ce5d8b3fc7f","mcp_get_code":{"code_sha256":"2b3e6ce5d8b3fc7f"}},{"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":"ewinata/CIFAR-10","path":"resnet13/cifar_10_resnet13.py","file_url":"https://github.com/ewinata/CIFAR-10/blob/HEAD/resnet13/cifar_10_resnet13.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":"489084bcff558f3e","mcp_get_code":{"code_sha256":"489084bcff558f3e"}},{"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":"vgangal101/resnet_models","path":"models/resnet_imgnt.py","file_url":"https://github.com/vgangal101/resnet_models/blob/HEAD/models/resnet_imgnt.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e08a40ca144f258c","mcp_get_code":{"code_sha256":"e08a40ca144f258c"}},{"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":"manoranjan03/PaperImplementations","path":"RelevantModelCIFAR.py","file_url":"https://github.com/manoranjan03/PaperImplementations/blob/HEAD/RelevantModelCIFAR.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e557f59178c9728","mcp_get_code":{"code_sha256":"4e557f59178c9728"}},{"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":"fchollet/deep-learning-models","path":"resnet50.py","file_url":"https://github.com/fchollet/deep-learning-models/blob/HEAD/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e7a557c615eb17e","mcp_get_code":{"code_sha256":"5e7a557c615eb17e"}},{"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":"ajjdan/KaI","path":"Model.py","file_url":"https://github.com/ajjdan/KaI/blob/HEAD/Model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"none","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e24e6b96a2583167","mcp_get_code":{"code_sha256":"e24e6b96a2583167"}},{"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":"nikhilroxtomar/semantic-segmentation-architecture","path":"PyTorch/unet.py","file_url":"https://github.com/nikhilroxtomar/semantic-segmentation-architecture/blob/HEAD/PyTorch/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6968ac72dcd06a06","mcp_get_code":{"code_sha256":"6968ac72dcd06a06"}},{"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":"bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets","path":"Models.py","file_url":"https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets/blob/HEAD/Models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f12eaf2d9d817665","mcp_get_code":{"code_sha256":"f12eaf2d9d817665"}},{"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":"BboyHanat/U-Net","path":"network.py","file_url":"https://github.com/BboyHanat/U-Net/blob/HEAD/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc048a040f09c706","mcp_get_code":{"code_sha256":"fc048a040f09c706"}},{"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":"devanshpratapsingh/U-net_for_Multiclass_Segmentation","path":"architecture.py","file_url":"https://github.com/devanshpratapsingh/U-net_for_Multiclass_Segmentation/blob/HEAD/architecture.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":"e697c265401d10d1","mcp_get_code":{"code_sha256":"e697c265401d10d1"}},{"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":"nikhilroxtomar/Unet-for-Person-Segmentation","path":"model.py","file_url":"https://github.com/nikhilroxtomar/Unet-for-Person-Segmentation/blob/HEAD/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e46bedb0bcbfa49","mcp_get_code":{"code_sha256":"4e46bedb0bcbfa49"}},{"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":"rizalmaulanaa/robustness_of_prob_u_net","path":"Models/builders/UNet.py","file_url":"https://github.com/rizalmaulanaa/robustness_of_prob_u_net/blob/HEAD/Models/builders/UNet.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":"9c13a473bd7c1353","mcp_get_code":{"code_sha256":"9c13a473bd7c1353"}},{"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":"SahinTiryaki/Brain-tumor-segmentation-Vgg19UNet","path":"model.py","file_url":"https://github.com/SahinTiryaki/Brain-tumor-segmentation-Vgg19UNet/blob/HEAD/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":"c3583f5940e1f432","mcp_get_code":{"code_sha256":"c3583f5940e1f432"}},{"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":"FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope","path":"src/ml/model.py","file_url":"https://github.com/FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope/blob/HEAD/src/ml/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"599c8f170b4a52c2","mcp_get_code":{"code_sha256":"599c8f170b4a52c2"}},{"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":"lurenhaothu/CWMI","path":"model/att_unet.py","file_url":"https://github.com/lurenhaothu/CWMI/blob/HEAD/model/att_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5716150b612c2bc8","mcp_get_code":{"code_sha256":"5716150b612c2bc8"}},{"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":"1044197988/TF.Keras-Commonly-used-models","path":"常用分割模型/Unet_family/Unet_family.py","file_url":"https://github.com/1044197988/TF.Keras-Commonly-used-models/blob/HEAD/%E5%B8%B8%E7%94%A8%E5%88%86%E5%89%B2%E6%A8%A1%E5%9E%8B/Unet_family/Unet_family.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"15d2c13563dfde6c","mcp_get_code":{"code_sha256":"15d2c13563dfde6c"}},{"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":"reenal/melanoma-skin-cancer-image-segmentation","path":"model.py","file_url":"https://github.com/reenal/melanoma-skin-cancer-image-segmentation/blob/HEAD/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":"c914c2fd28c84b87","mcp_get_code":{"code_sha256":"c914c2fd28c84b87"}},{"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":"FelixGruen/tensorflow-u-net","path":"architecture/networks.py","file_url":"https://github.com/FelixGruen/tensorflow-u-net/blob/HEAD/architecture/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c2ae69cf2fe659ec","mcp_get_code":{"code_sha256":"c2ae69cf2fe659ec"}},{"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":"harsh-jadhav/SIIM-Pneumothorax-Case-Study2","path":"Deployable Code/model.py","file_url":"https://github.com/harsh-jadhav/SIIM-Pneumothorax-Case-Study2/blob/HEAD/Deployable%20Code/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f125c9df5118dd63","mcp_get_code":{"code_sha256":"f125c9df5118dd63"}},{"arxiv_id":"1503.02531","paper":"/paper/distilling-the-knowledge-in-a-neural-network","title":"Distilling the Knowledge in a Neural Network","date":"2015-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wonbeomjang/Knowledge-Distilling-PyTorch","path":"models.py","file_url":"https://github.com/wonbeomjang/Knowledge-Distilling-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":"0a330946ecfac712","mcp_get_code":{"code_sha256":"0a330946ecfac712"}},{"arxiv_id":"1409.4842","paper":"/paper/going-deeper-with-convolutions","title":"Going Deeper with Convolutions","date":"2014-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjfghk5697/Pytorch-Research-Paper-Implementations","path":"Vision Models/GoogleNet/models/stl_model.py","file_url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/blob/HEAD/Vision%20Models/GoogleNet/models/stl_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30e38658fd407250","mcp_get_code":{"code_sha256":"30e38658fd407250"}},{"arxiv_id":"1406.2199","paper":"/paper/two-stream-convolutional-networks-for-action","title":"Two-Stream Convolutional Networks for Action Recognition in Videos","date":"2014-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"woodfrog/ActionRecognition","path":"models/resnet50.py","file_url":"https://github.com/woodfrog/ActionRecognition/blob/HEAD/models/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e7a557c615eb17e","mcp_get_code":{"code_sha256":"5e7a557c615eb17e"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75200f77df62e20b","mcp_get_code":{"code_sha256":"75200f77df62e20b"}},{"arxiv_id":"aaai_28229","paper":null,"title":"arXiv:aaai_28229","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"eezkni/ColNeRF","path":"src/model/InterviewAttention.py","file_url":"https://github.com/eezkni/ColNeRF/blob/HEAD/src/model/InterviewAttention.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":"c530c864a22f1124","mcp_get_code":{"code_sha256":"c530c864a22f1124"}},{"arxiv_id":"aaai_25934","paper":null,"title":"arXiv:aaai_25934","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"arghosh/DiFA","path":"src/models/layers.py","file_url":"https://github.com/arghosh/DiFA/blob/HEAD/src/models/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2a5fdc0a059abac","mcp_get_code":{"code_sha256":"e2a5fdc0a059abac"}},{"arxiv_id":"aaai_20745","paper":null,"title":"arXiv:aaai_20745","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"dmdmello/HC-MGAN","path":"models/utils.py","file_url":"https://github.com/dmdmello/HC-MGAN/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d48fdd69fa9b81b1","mcp_get_code":{"code_sha256":"d48fdd69fa9b81b1"}},{"arxiv_id":"2020.emnlp-demos.29","paper":null,"title":"arXiv:2020.emnlp-demos.29","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/isobs","path":"source/ProtoNet.py","file_url":"https://github.com/thunlp/isobs/blob/HEAD/source/ProtoNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4018d80d0425da98","mcp_get_code":{"code_sha256":"4018d80d0425da98"}}]}