{"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/resnet34-2","entry":"ResNet34","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":91,"n_papers_ran":41,"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":78,"n_samples_ran":35,"n_samples_fingerprinted":0,"n_places":93,"n_places_pointer_only":31,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":9,"ran_fixture":0,"ran":26,"unverified":43},"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":"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":"Jiacheng8/FADRM","path":"models/resnet.py","file_url":"https://github.com/Jiacheng8/FADRM/blob/HEAD/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":"f9e75567dfd01367","mcp_get_code":{"code_sha256":"f9e75567dfd01367"}},{"arxiv_id":"2603.05116","paper":"/paper/arxiv-2603-05116","title":"FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning","date":"2026-03-05","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"junkangLiu0/FedBCGD","path":"models/resnet.py","file_url":"https://github.com/junkangLiu0/FedBCGD/blob/HEAD/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":"779e90045d3680d8","mcp_get_code":{"code_sha256":"779e90045d3680d8"}},{"arxiv_id":"2603.04731","paper":"/paper/arxiv-2603-04731","title":"When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"zhli-cs/BAIT","path":"models/ResNet.py","file_url":"https://github.com/zhli-cs/BAIT/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2510.13451","paper":"/paper/arxiv-2510-13451","title":"Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"BaiLibl/ShadowPool","path":"models/resnet.py","file_url":"https://github.com/BaiLibl/ShadowPool/blob/HEAD/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":"bd5133ec748acd0f","mcp_get_code":{"code_sha256":"bd5133ec748acd0f"}},{"arxiv_id":"2510.08836","paper":"/paper/arxiv-2510-08836","title":"Long-Tailed Recognition via Information-Preservable Two-Stage Learning","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"fudong03/BNS_IPDPP","path":"models/models_resnet.py","file_url":"https://github.com/fudong03/BNS_IPDPP/blob/HEAD/models/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":"01625650e8212242","mcp_get_code":{"code_sha256":"01625650e8212242"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a770e2ca7cc21944","mcp_get_code":{"code_sha256":"a770e2ca7cc21944"}},{"arxiv_id":"2506.24125","paper":"/paper/fadrm-fast-and-accurate-data-residual","title":"FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation","date":"2025-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiacheng8/fadrm","path":"models/resnet.py","file_url":"https://github.com/jiacheng8/fadrm/blob/HEAD/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":"f9e75567dfd01367","mcp_get_code":{"code_sha256":"f9e75567dfd01367"}},{"arxiv_id":"2505.18514","paper":"/paper/test-time-adaptation-with-binary-feedback","title":"Test-Time Adaptation with Binary Feedback","date":"2025-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taeckyung/BiTTA","path":"models/ResNet.py","file_url":"https://github.com/taeckyung/BiTTA/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":"0db2a6b94d888b0e","mcp_get_code":{"code_sha256":"0db2a6b94d888b0e"}},{"arxiv_id":"2505.04775","paper":"/paper/prediction-via-shapley-value-regression","title":"Prediction via Shapley Value Regression","date":"2025-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amrmalkhatib/ViaSHAP","path":"resshap.py","file_url":"https://github.com/amrmalkhatib/ViaSHAP/blob/HEAD/resshap.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a9a780e3089ffb0","mcp_get_code":{"code_sha256":"1a9a780e3089ffb0"}},{"arxiv_id":"2501.07575","paper":"/paper/dataset-distillation-via-committee-voting","title":"Dataset Distillation via Committee Voting","date":"2025-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiacheng8/cv-dd","path":"models/resnet.py","file_url":"https://github.com/jiacheng8/cv-dd/blob/HEAD/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":"f9e75567dfd01367","mcp_get_code":{"code_sha256":"f9e75567dfd01367"}},{"arxiv_id":"2411.16162","paper":"/paper/sparse-patches-adversarial-attacks-via","title":"Sparse patches adversarial attacks via extrapolating point-wise information","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanemcovsky/sparsepatches","path":"models/cifar10/resnet.py","file_url":"https://github.com/yanemcovsky/sparsepatches/blob/HEAD/models/cifar10/resnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"92df69bfd3a2a190","mcp_get_code":{"code_sha256":"92df69bfd3a2a190"}},{"arxiv_id":"2411.16162","paper":"/paper/sparse-patches-adversarial-attacks-via","title":"Sparse patches adversarial attacks via extrapolating point-wise information","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanemcovsky/SparsePatches","path":"models/cifar10/resnet.py","file_url":"https://github.com/yanemcovsky/SparsePatches/blob/HEAD/models/cifar10/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a44db322b52fd5bb","mcp_get_code":{"code_sha256":"a44db322b52fd5bb"}},{"arxiv_id":"2409.01832","paper":"/paper/beyond-unconstrained-features-neural-collapse","title":"Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data","date":"2024-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wanlihongc/neural-collapse","path":"train_utils.py","file_url":"https://github.com/wanlihongc/neural-collapse/blob/HEAD/train_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79c1cacca9bb2e2d","mcp_get_code":{"code_sha256":"79c1cacca9bb2e2d"}},{"arxiv_id":"2408.06742","paper":"/paper/long-tailed-out-of-distribution-detection","title":"Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to Tail","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inar-design/patt","path":"models/resnet.py","file_url":"https://github.com/inar-design/patt/blob/HEAD/models/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":"8de82e83c702450c","mcp_get_code":{"code_sha256":"8de82e83c702450c"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a03b2b229d94786","mcp_get_code":{"code_sha256":"3a03b2b229d94786"}},{"arxiv_id":"2407.18365","paper":"/paper/fadas-towards-federated-adaptive-asynchronous","title":"FADAS: Towards Federated Adaptive Asynchronous Optimization","date":"2024-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yujiaw98/FADAS","path":"models/resnet.py","file_url":"https://github.com/yujiaw98/FADAS/blob/HEAD/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"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":"eee26fa0aef03470","mcp_get_code":{"code_sha256":"eee26fa0aef03470"}},{"arxiv_id":"2406.03057","paper":"/paper/bws-best-window-selection-based-on-sample","title":"BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NohyunKi/BWS","path":"model/resnet.py","file_url":"https://github.com/NohyunKi/BWS/blob/HEAD/model/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"38d8a6134f95e3ec","mcp_get_code":{"code_sha256":"38d8a6134f95e3ec"}},{"arxiv_id":"2406.01494","paper":"/paper/robust-classification-by-coupling-data","title":"Robust Classification by Coupling Data Mollification with Label Smoothing","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"markusheinonen/supervised-mollification","path":"src/networks/resnet.py","file_url":"https://github.com/markusheinonen/supervised-mollification/blob/HEAD/src/networks/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1756cb6f79386fd3","mcp_get_code":{"code_sha256":"1756cb6f79386fd3"}},{"arxiv_id":"2405.03316","paper":"/paper/provably-unlearnable-examples","title":"Provably Unlearnable Data Examples","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuralsec/certified-data-learnability","path":"models/ResNet.py","file_url":"https://github.com/neuralsec/certified-data-learnability/blob/HEAD/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"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.py","file_url":"https://github.com/yuyi-sd/D-VAE/blob/HEAD/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":"b2e6f658713469be","mcp_get_code":{"code_sha256":"b2e6f658713469be"}},{"arxiv_id":"2403.06668","paper":"/paper/peeraid-improving-adversarial-distillation","title":"PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaewonalive/peeraid","path":"models/resnet.py","file_url":"https://github.com/jaewonalive/peeraid/blob/HEAD/models/resnet.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":"d6b55f16ed793757","mcp_get_code":{"code_sha256":"d6b55f16ed793757"}},{"arxiv_id":"2403.06659","paper":"/paper/zero-shot-ecg-classification-with-multimodal","title":"Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cheliu-computation/merl","path":"finetune/models/resnet1d.py","file_url":"https://github.com/cheliu-computation/merl/blob/HEAD/finetune/models/resnet1d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d972ff40178697e","mcp_get_code":{"code_sha256":"4d972ff40178697e"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a89d570c0420fa68","mcp_get_code":{"code_sha256":"a89d570c0420fa68"}},{"arxiv_id":"2402.14430","paper":"/paper/robust-training-of-federated-models-with","title":"Robust Training of Federated Models with Extremely Label Deficiency","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/Twin-sight","path":"model/SSFL_ResNet18.py","file_url":"https://github.com/tmlr-group/Twin-sight/blob/HEAD/model/SSFL_ResNet18.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b648d9508676e89","mcp_get_code":{"code_sha256":"1b648d9508676e89"}},{"arxiv_id":"2402.07839","paper":"/paper/towards-meta-pruning-via-optimal-transport","title":"Towards Meta-Pruning via Optimal Transport","date":"2024-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandertheus/intra-fusion","path":"model_architectures/resnet.py","file_url":"https://github.com/alexandertheus/intra-fusion/blob/HEAD/model_architectures/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb7cde1fc11439bc","mcp_get_code":{"code_sha256":"cb7cde1fc11439bc"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"da291a22483b4cb4","mcp_get_code":{"code_sha256":"da291a22483b4cb4"}},{"arxiv_id":"2312.14126","paper":"/paper/entropic-open-set-active-learning","title":"Entropic Open-set Active Learning","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bardisafa/eoal","path":"resnet.py","file_url":"https://github.com/bardisafa/eoal/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":"c0fc81d580cc0e4f","mcp_get_code":{"code_sha256":"c0fc81d580cc0e4f"}},{"arxiv_id":"2312.13555","paper":"/paper/cr-sam-curvature-regularized-sharpness-aware","title":"CR-SAM: Curvature Regularized Sharpness-Aware Minimization","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustaiot/cr-sam","path":"models/resnet.py","file_url":"https://github.com/trustaiot/cr-sam/blob/HEAD/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":"9e5e3e4cd9d178d9","mcp_get_code":{"code_sha256":"9e5e3e4cd9d178d9"}},{"arxiv_id":"2312.10686","paper":"/paper/out-of-distribution-detection-in-long-tailed","title":"Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning","date":"2023-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mala-lab/cocl","path":"models/resnet.py","file_url":"https://github.com/mala-lab/cocl/blob/HEAD/models/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":"f0d6d8f321537b23","mcp_get_code":{"code_sha256":"f0d6d8f321537b23"}},{"arxiv_id":"2312.08939","paper":"/paper/eat-towards-long-tailed-out-of-distribution","title":"EAT: Towards Long-Tailed Out-of-Distribution Detection","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stomach-ache/long-tailed-ood-detection","path":"models/our_resnet.py","file_url":"https://github.com/stomach-ache/long-tailed-ood-detection/blob/HEAD/models/our_resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"902136f1bf7317f5","mcp_get_code":{"code_sha256":"902136f1bf7317f5"}},{"arxiv_id":"2312.08898","paper":"/paper/detection-and-defense-of-unlearnable-examples","title":"Detection and Defense of Unlearnable Examples","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hala64/udp","path":"model/ResNet.py","file_url":"https://github.com/hala64/udp/blob/HEAD/model/ResNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea4d8c8416d65342","mcp_get_code":{"code_sha256":"ea4d8c8416d65342"}},{"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":"sparselearning/models.py","file_url":"https://github.com/zhao1402072392/rest/blob/HEAD/sparselearning/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69f1881e9527c23c","mcp_get_code":{"code_sha256":"69f1881e9527c23c"}},{"arxiv_id":"2312.00761","paper":"/paper/deep-unlearning-fast-and-efficient-training","title":"Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sangamesh-kodge/class_forgetting","path":"models/resnet.py","file_url":"https://github.com/sangamesh-kodge/class_forgetting/blob/HEAD/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":"b0429bb7e020f5fb","mcp_get_code":{"code_sha256":"b0429bb7e020f5fb"}},{"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/ResNet.py","file_url":"https://github.com/jhoon-oh/FedBABU/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2310.18910","paper":"/paper/instant-semi-supervised-learning-with","title":"InstanT: Semi-supervised Learning with Instance-dependent Thresholds","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmllab/2023_NeurIPS_InstanT","path":"semilearn/algorithms/adamatch/T_estimator.py","file_url":"https://github.com/tmllab/2023_NeurIPS_InstanT/blob/HEAD/semilearn/algorithms/adamatch/T_estimator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc902b5d29331c9e","mcp_get_code":{"code_sha256":"cc902b5d29331c9e"}},{"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.py","file_url":"https://github.com/MediaBrain-SJTU/Geometric-Harmonization/blob/HEAD/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c2b0a60d47724297","mcp_get_code":{"code_sha256":"c2b0a60d47724297"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"20a02fb684e52cb6","mcp_get_code":{"code_sha256":"20a02fb684e52cb6"}},{"arxiv_id":"2310.04361","paper":"/paper/exploiting-transformer-activation-sparsity","title":"Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bartwojcik/d2dmoe","path":"architectures/resnets.py","file_url":"https://github.com/bartwojcik/d2dmoe/blob/HEAD/architectures/resnets.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"724da9ddbe842c71","mcp_get_code":{"code_sha256":"724da9ddbe842c71"}},{"arxiv_id":"2309.04195","paper":"/paper/towards-mitigating-architecture-overfitting","title":"Towards Mitigating Architecture Overfitting on Distilled Datasets","date":"2023-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cityu-mlo/mitigate_architecture_overfitting","path":"model.py","file_url":"https://github.com/cityu-mlo/mitigate_architecture_overfitting/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":"85d94fdef1cf29c2","mcp_get_code":{"code_sha256":"85d94fdef1cf29c2"}},{"arxiv_id":"2308.14831","paper":"/paper/continual-learning-with-dynamic-sparse","title":"Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muratonuryildirim/cl-with-dst","path":"models.py","file_url":"https://github.com/muratonuryildirim/cl-with-dst/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35a348081e80bce5","mcp_get_code":{"code_sha256":"35a348081e80bce5"}},{"arxiv_id":"2308.13862","paper":"/paper/late-stopping-avoiding-confidently-learning","title":"Late Stopping: Avoiding Confidently Learning from Mislabeled Examples","date":"2023-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmllab/2023_ICCV_LateStopping","path":"LateStopping/resnetnew.py","file_url":"https://github.com/tmllab/2023_ICCV_LateStopping/blob/HEAD/LateStopping/resnetnew.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"10a2ee96b82870d4","mcp_get_code":{"code_sha256":"10a2ee96b82870d4"}},{"arxiv_id":"2307.13885","paper":"/paper/efficient-estimation-of-the-local-robustness","title":"Characterizing Data Point Vulnerability via Average-Case Robustness","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4life-group/average-case-robustness","path":"models/resnet.py","file_url":"https://github.com/ai4life-group/average-case-robustness/blob/HEAD/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":"333bf028a3aa2d1f","mcp_get_code":{"code_sha256":"333bf028a3aa2d1f"}},{"arxiv_id":"2303.08500","paper":"/paper/the-devil-s-advocate-shattering-the-illusion","title":"The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmdolatabadi/avatar","path":"models/ResNet.py","file_url":"https://github.com/hmdolatabadi/avatar/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":"d8df895be3c7b30e","mcp_get_code":{"code_sha256":"d8df895be3c7b30e"}},{"arxiv_id":"2303.02251","paper":"/paper/certified-robust-neural-networks","title":"Certified Robust Neural Networks: Generalization and Corruption Resistance","date":"2023-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanlucas3/hr_neural_networks","path":"HR_Neural_Networks/Paper_experiments/Section_6.3/resnet.py","file_url":"https://github.com/ryanlucas3/hr_neural_networks/blob/HEAD/HR_Neural_Networks/Paper_experiments/Section_6.3/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72f12a77446ad832","mcp_get_code":{"code_sha256":"72f12a77446ad832"}},{"arxiv_id":"2302.03357","paper":"/paper/towards-better-time-series-contrastive","title":"Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach","date":"2023-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lanxiang1017/dynamicbadpairmining_iclr24","path":"models/dbpm_model.py","file_url":"https://github.com/lanxiang1017/dynamicbadpairmining_iclr24/blob/HEAD/models/dbpm_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":"3a802f69ecd390d5","mcp_get_code":{"code_sha256":"3a802f69ecd390d5"}},{"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":"wakings/tabasco","path":"models/PreResNet.py","file_url":"https://github.com/wakings/tabasco/blob/HEAD/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":"eee26fa0aef03470","mcp_get_code":{"code_sha256":"eee26fa0aef03470"}},{"arxiv_id":"2208.08270","paper":"/paper/on-the-privacy-effect-of-data-enhancement-via","title":"On the Privacy Effect of Data Enhancement via the Lens of Memorization","date":"2022-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lixiaothu/privacy_and_aug","path":"models/ResNet.py","file_url":"https://github.com/lixiaothu/privacy_and_aug/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":"bdfee6e9a24e258f","mcp_get_code":{"code_sha256":"bdfee6e9a24e258f"}},{"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":"eee26fa0aef03470","mcp_get_code":{"code_sha256":"eee26fa0aef03470"}},{"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.py","file_url":"https://github.com/cnc-ood/cnc_ood/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":"135e97055dfaf8bd","mcp_get_code":{"code_sha256":"135e97055dfaf8bd"}},{"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":"acmi-lab/open-set-label-shift","path":"models/Resnet.py","file_url":"https://github.com/acmi-lab/open-set-label-shift/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":"448400e6aad91289","mcp_get_code":{"code_sha256":"448400e6aad91289"}},{"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/ResNet.py","file_url":"https://github.com/cychomatica/one-pixel-shotcut/blob/HEAD/model/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":"ca1d217d7ca5ddf4","mcp_get_code":{"code_sha256":"ca1d217d7ca5ddf4"}},{"arxiv_id":"2205.11506","paper":"/paper/orchestra-unsupervised-federated-learning-via","title":"Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering","date":"2022-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akhilmathurs/orchestra","path":"models.py","file_url":"https://github.com/akhilmathurs/orchestra/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":"37d17635667d7654","mcp_get_code":{"code_sha256":"37d17635667d7654"}},{"arxiv_id":"2204.02078","paper":"/paper/semi-supervised-semantic-segmentation-with-5","title":"Semi-supervised Semantic Segmentation with Error Localization Network","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kinux98/SSL_ELN","path":"modeling/ELN.py","file_url":"https://github.com/kinux98/SSL_ELN/blob/HEAD/modeling/ELN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"40e0da40c8d014b0","mcp_get_code":{"code_sha256":"40e0da40c8d014b0"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"837fb968d323f5b6","mcp_get_code":{"code_sha256":"837fb968d323f5b6"}},{"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_tiny.py","file_url":"https://github.com/nazmul-karim170/unicon-noisy-label/blob/HEAD/PreResNet_tiny.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"303627d2a451896d","mcp_get_code":{"code_sha256":"303627d2a451896d"}},{"arxiv_id":"2112.02612","paper":"/paper/training-structured-neural-networks-through-1","title":"Training Structured Neural Networks Through Manifold Identification and Variance Reduction","date":"2021-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zihsyuan1214/rmda","path":"Experiments/Models/resnet50.py","file_url":"https://github.com/zihsyuan1214/rmda/blob/HEAD/Experiments/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":"10a3e7cea064b5d7","mcp_get_code":{"code_sha256":"10a3e7cea064b5d7"}},{"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.py","file_url":"https://github.com/LijieFan/AdvCL/blob/HEAD/models/resnet_cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"94f4059c6a99ea7a","mcp_get_code":{"code_sha256":"94f4059c6a99ea7a"}},{"arxiv_id":"2110.09057","paper":"/paper/training-deep-neural-networks-with-adaptive","title":"Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kentaroy47/vision-transformers-cifar10","path":"models/resnet.py","file_url":"https://github.com/kentaroy47/vision-transformers-cifar10/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2109.13398","paper":"/paper/unrolling-sgd-understanding-factors","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","date":"2021-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleverhans-lab/unrolling-sgd","path":"ResNet_CIFAR10.py","file_url":"https://github.com/cleverhans-lab/unrolling-sgd/blob/HEAD/ResNet_CIFAR10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a28296ebc73a658","mcp_get_code":{"code_sha256":"9a28296ebc73a658"}},{"arxiv_id":"2109.05554","paper":"/paper/no-true-state-of-the-art-ood-detection","title":"No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets","date":"2021-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tajwarfahim/ood_detection_inconsistency","path":"utils/siamese_network.py","file_url":"https://github.com/tajwarfahim/ood_detection_inconsistency/blob/HEAD/utils/siamese_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":"9d4e412922385a20","mcp_get_code":{"code_sha256":"9d4e412922385a20"}},{"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/ResNet.py","file_url":"https://github.com/jhoon-oh/fedbabu/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2106.04928","paper":"/paper/reliable-adversarial-distillation-with","title":"Reliable Adversarial Distillation with Unreliable Teachers","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zfancy/iad","path":"models/resnet.py","file_url":"https://github.com/zfancy/iad/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":"9aac0eec28d31210","mcp_get_code":{"code_sha256":"9aac0eec28d31210"}},{"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":"eee26fa0aef03470","mcp_get_code":{"code_sha256":"eee26fa0aef03470"}},{"arxiv_id":"2105.13937","paper":"/paper/polygonal-unadjusted-langevin-algorithms","title":"Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks","date":"2021-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DongyoungLim/THEO_POULA","path":"models/resnet.py","file_url":"https://github.com/DongyoungLim/THEO_POULA/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2105.04319","paper":"/paper/a-bregman-learning-framework-for-sparse","title":"A Bregman Learning Framework for Sparse Neural Networks","date":"2021-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TimRoith/BregmanLearning","path":"models/resnet.py","file_url":"https://github.com/TimRoith/BregmanLearning/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":"3a99b51deb632de7","mcp_get_code":{"code_sha256":"3a99b51deb632de7"}},{"arxiv_id":"2011.10566","paper":"/paper/exploring-simple-siamese-representation","title":"Exploring Simple Siamese Representation Learning","date":"2020-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Reza-Safdari/SimSiam","path":"simsiam/model_factory.py","file_url":"https://github.com/Reza-Safdari/SimSiam/blob/HEAD/simsiam/model_factory.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":"efbcdfcbc565884f","mcp_get_code":{"code_sha256":"efbcdfcbc565884f"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2a3b85f8f4dd6d3","mcp_get_code":{"code_sha256":"b2a3b85f8f4dd6d3"}},{"arxiv_id":"2008.00627","paper":"/paper/learning-to-purify-noisy-labels-via-meta-soft","title":"Learning to Purify Noisy Labels via Meta Soft Label Corrector","date":"2020-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WuYichen-97/Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector","path":"resnet.py","file_url":"https://github.com/WuYichen-97/Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector/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":"3d76aed120d3f2a9","mcp_get_code":{"code_sha256":"3d76aed120d3f2a9"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e656d66272c6fd73","mcp_get_code":{"code_sha256":"e656d66272c6fd73"}},{"arxiv_id":"2006.15815","paper":"/paper/adai-separating-the-effects-of-adaptive","title":"Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia","date":"2020-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zeke-xie/adaptive-inertia-adai","path":"model/resnet.py","file_url":"https://github.com/zeke-xie/adaptive-inertia-adai/blob/HEAD/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"arxiv_id":"2006.13554","paper":"/paper/normalized-loss-functions-for-deep-learning","title":"Normalized Loss Functions for Deep Learning with Noisy Labels","date":"2020-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HanxunH/Active-Passive-Losses","path":"archive/model.py","file_url":"https://github.com/HanxunH/Active-Passive-Losses/blob/HEAD/archive/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18a4a9f249bb889a","mcp_get_code":{"code_sha256":"18a4a9f249bb889a"}},{"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":"a30de131b4f273c8","mcp_get_code":{"code_sha256":"a30de131b4f273c8"}},{"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":"VirajBagal/FMix-Paper-Implementation","path":"model.py","file_url":"https://github.com/VirajBagal/FMix-Paper-Implementation/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":"85d8c791298ea1f7","mcp_get_code":{"code_sha256":"85d8c791298ea1f7"}},{"arxiv_id":"2002.06349","paper":"/paper/hold-me-tight-influence-of-discriminative","title":"Hold me tight! Influence of discriminative features on deep network boundaries","date":"2020-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LTS4/hold-me-tight","path":"model_classes/mnist/resnet.py","file_url":"https://github.com/LTS4/hold-me-tight/blob/HEAD/model_classes/mnist/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":"6c239a83e0919035","mcp_get_code":{"code_sha256":"6c239a83e0919035"}},{"arxiv_id":"1911.07471","paper":"/paper/preparing-lessons-improve-knowledge","title":"Preparing Lessons: Improve Knowledge Distillation with Better Supervision","date":"2019-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SforAiDl/KD_Lib","path":"KD_Lib/models/resnet.py","file_url":"https://github.com/SforAiDl/KD_Lib/blob/HEAD/KD_Lib/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":"86c26da00529c65b","mcp_get_code":{"code_sha256":"86c26da00529c65b"}},{"arxiv_id":"1911.03584","paper":"/paper/on-the-relationship-between-self-attention-1","title":"On the Relationship between Self-Attention and Convolutional Layers","date":"2019-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"epfml/attention-cnn","path":"models/resnet.py","file_url":"https://github.com/epfml/attention-cnn/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":"af3a6e8a2702592f","mcp_get_code":{"code_sha256":"af3a6e8a2702592f"}},{"arxiv_id":"1911.00068","paper":"/paper/confident-learning-estimating-uncertainty-in","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","date":"2019-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chang-yue/ctrl","path":"cifar10/models/resnet.py","file_url":"https://github.com/chang-yue/ctrl/blob/HEAD/cifar10/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":"f9bdc20fe8505a75","mcp_get_code":{"code_sha256":"f9bdc20fe8505a75"}},{"arxiv_id":"1905.13613","paper":"/paper/subspace-networks-for-few-shot-classification","title":"Regression Networks for Meta-Learning Few-Shot Classification","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ArnoutDevos/RegressionNet","path":"backbone.py","file_url":"https://github.com/ArnoutDevos/RegressionNet/blob/HEAD/backbone.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ff6c4f8a05abb1d3","mcp_get_code":{"code_sha256":"ff6c4f8a05abb1d3"}},{"arxiv_id":"1905.11926","paper":"/paper/190511926","title":"Network Deconvolution","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deconvolutionpaper/deconvolution","path":"models/resnet.py","file_url":"https://github.com/deconvolutionpaper/deconvolution/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":"80b2eaf0a7fdbbb4","mcp_get_code":{"code_sha256":"80b2eaf0a7fdbbb4"}},{"arxiv_id":"1905.09747","paper":"/paper/190509747","title":"Adversarially Robust Distillation","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goldblum/AdversariallyRobustDistillation","path":"models/resnet.py","file_url":"https://github.com/goldblum/AdversariallyRobustDistillation/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":"9aac0eec28d31210","mcp_get_code":{"code_sha256":"9aac0eec28d31210"}},{"arxiv_id":"1904.03955","paper":"/paper/kervolutional-neural-networks","title":"Kervolutional Neural Networks","date":"2019-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuch37/Kerception","path":"models/resnet.py","file_url":"https://github.com/liuch37/Kerception/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":"0739a591b13f79cf","mcp_get_code":{"code_sha256":"0739a591b13f79cf"}},{"arxiv_id":"1901.00544","paper":"/paper/multi-class-classification-without-multi","title":"Multi-class Classification without Multi-class Labels","date":"2019-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GT-RIPL/L2C","path":"models/resnet.py","file_url":"https://github.com/GT-RIPL/L2C/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":"10e7c9fb11ee37d6","mcp_get_code":{"code_sha256":"10e7c9fb11ee37d6"}},{"arxiv_id":"1811.12814","paper":"/paper/graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChriXiang/GloRe_pytorch","path":"resnet_example.py","file_url":"https://github.com/ChriXiang/GloRe_pytorch/blob/HEAD/resnet_example.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":"e39cd693a1b17d14","mcp_get_code":{"code_sha256":"e39cd693a1b17d14"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e9e6f460fea80bb","mcp_get_code":{"code_sha256":"3e9e6f460fea80bb"}},{"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/models2d.py","file_url":"https://github.com/lxdv/ecg-classification/blob/HEAD/models/models2d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e6d1bd0db767e63","mcp_get_code":{"code_sha256":"6e6d1bd0db767e63"}},{"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/resnet.py","file_url":"https://github.com/thughost2/Padam/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":"ac3dd06d1a08c549","mcp_get_code":{"code_sha256":"ac3dd06d1a08c549"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da6a1c8dca7ef4c2","mcp_get_code":{"code_sha256":"da6a1c8dca7ef4c2"}},{"arxiv_id":"aaai_33972","paper":null,"title":"arXiv:aaai_33972","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sangamesh-kodge/SAP","path":"models/resnet.py","file_url":"https://github.com/sangamesh-kodge/SAP/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":"a5f542287fef57fe","mcp_get_code":{"code_sha256":"a5f542287fef57fe"}},{"arxiv_id":"aaai_28217","paper":null,"title":"arXiv:aaai_28217","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mala-lab/COCL","path":"models/resnet.py","file_url":"https://github.com/mala-lab/COCL/blob/HEAD/models/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":"f0d6d8f321537b23","mcp_get_code":{"code_sha256":"f0d6d8f321537b23"}},{"arxiv_id":"aaai_28019","paper":null,"title":"arXiv:aaai_28019","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VinAIResearch/COMBAT","path":"classifier_models/resnet.py","file_url":"https://github.com/VinAIResearch/COMBAT/blob/HEAD/classifier_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":"d563c06d8c1c752b","mcp_get_code":{"code_sha256":"d563c06d8c1c752b"}},{"arxiv_id":"aaai_26027","paper":null,"title":"arXiv:aaai_26027","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"UCAS-LCH/Twin-Rep","path":"models/resnet.py","file_url":"https://github.com/UCAS-LCH/Twin-Rep/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":"e3ff5081e5907cec","mcp_get_code":{"code_sha256":"e3ff5081e5907cec"}},{"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/resnet.py","file_url":"https://github.com/MCG-NJU/DDM/blob/HEAD/DDM-Net/modeling/resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05100e051ccf61f0","mcp_get_code":{"code_sha256":"05100e051ccf61f0"}}]}