{"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/mixup-data","entry":"mixup_data","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":51,"n_papers_ran":40,"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":35,"n_samples_ran":22,"n_samples_fingerprinted":5,"n_places":59,"n_places_pointer_only":29,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":17,"ran":2,"unverified":13},"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":"2606.04971","paper":"/paper/arxiv-2606-04971","title":"Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"anna-richter/be-fair","path":"aide/logs/17-addition_2/best_solution.py","file_url":"https://github.com/anna-richter/be-fair/blob/HEAD/aide/logs/17-addition_2/best_solution.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":"fc5b29d5049847a0","mcp_get_code":{"code_sha256":"fc5b29d5049847a0"}},{"arxiv_id":"2511.17914","paper":"/paper/arxiv-2511-17914","title":"Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"j-cyoung/ADSA_DD","path":"SRe2L/cifar10/relabel_cifar_adsa.py","file_url":"https://github.com/j-cyoung/ADSA_DD/blob/HEAD/SRe2L/cifar10/relabel_cifar_adsa.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5d6df350b6eefccd","mcp_get_code":{"code_sha256":"5d6df350b6eefccd"}},{"arxiv_id":"2509.26045","paper":"/paper/arxiv-2509-26045","title":"Scaling Up Temporal Domain Generalization via Temporal Experts Averaging","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"zxcvfd13502/TEA","path":"methods/mixup.py","file_url":"https://github.com/zxcvfd13502/TEA/blob/HEAD/methods/mixup.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e21ef67063f634df","mcp_get_code":{"code_sha256":"e21ef67063f634df"}},{"arxiv_id":"2409.17612","paper":"/paper/diversity-driven-synthesis-enhancing-dataset","title":"Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"angusdujw/diversity-driven-synthesis","path":"validation/validation_cifar.py","file_url":"https://github.com/angusdujw/diversity-driven-synthesis/blob/HEAD/validation/validation_cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5d6df350b6eefccd","mcp_get_code":{"code_sha256":"5d6df350b6eefccd"}},{"arxiv_id":"2407.19308","paper":"/paper/comprehensive-attribution-inherently","title":"Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector","date":"2024-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zood123/comet","path":"train_module.py","file_url":"https://github.com/zood123/comet/blob/HEAD/train_module.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8897714b1e9b6396","mcp_get_code":{"code_sha256":"8897714b1e9b6396"}},{"arxiv_id":"2405.01817","paper":"/paper/uniformly-stable-algorithms-for-adversarial","title":"Uniformly Stable Algorithms for Adversarial Training and Beyond","date":"2024-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiancongxiao/moreau-envelope-sgd","path":"adversarial_robustness_overfitting/mea_train_cifar.py","file_url":"https://github.com/jiancongxiao/moreau-envelope-sgd/blob/HEAD/adversarial_robustness_overfitting/mea_train_cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2403.06741","paper":"/paper/distribution-aware-data-expansion-with","title":"Distribution-Aware Data Expansion with Diffusion Models","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoweiz23/distdiff","path":"augmentations/mixup.py","file_url":"https://github.com/haoweiz23/distdiff/blob/HEAD/augmentations/mixup.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2312.02829","paper":"/paper/mimonets-multiple-input-multiple-output-1","title":"MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/multiple-input-multiple-output-nets","path":"MIMOConv/src/mixup.py","file_url":"https://github.com/IBM/multiple-input-multiple-output-nets/blob/HEAD/MIMOConv/src/mixup.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":"112a7683003fa463","mcp_get_code":{"code_sha256":"112a7683003fa463"}},{"arxiv_id":"2310.19342","paper":"/paper/label-only-model-inversion-attacks-via-1","title":"Label-Only Model Inversion Attacks via Knowledge Transfer","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"val-iisc/hard-label-model-stealing","path":"code/train_student/train_student.py","file_url":"https://github.com/val-iisc/hard-label-model-stealing/blob/HEAD/code/train_student/train_student.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9917ee672087e236","mcp_get_code":{"code_sha256":"9917ee672087e236"}},{"arxiv_id":"2306.06963","paper":"/paper/feature-fusion-from-head-to-tail-an-extreme","title":"Feature Fusion from Head to Tail for Long-Tailed Visual Recognition","date":"2023-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keke921/h2t","path":"methods.py","file_url":"https://github.com/keke921/h2t/blob/HEAD/methods.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2303.09447","paper":"/paper/steering-prototype-with-prompt-tuning-for","title":"Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning","date":"2023-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzvv123456/contrastive-prototypical-prompt","path":"utils.py","file_url":"https://github.com/lzvv123456/contrastive-prototypical-prompt/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2303.05506","paper":"/paper/tangos-regularizing-tabular-neural-networks","title":"TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alanjeffares/tangos","path":"src/regularizers.py","file_url":"https://github.com/alanjeffares/tangos/blob/HEAD/src/regularizers.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f8d74fe1d88a952d","mcp_get_code":{"code_sha256":"f8d74fe1d88a952d"}},{"arxiv_id":"2303.05506","paper":"/paper/tangos-regularizing-tabular-neural-networks","title":"TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vanderschaarlab/tangos","path":"src/tangos/regularizers.py","file_url":"https://github.com/vanderschaarlab/tangos/blob/HEAD/src/tangos/regularizers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"04a79674a945ae82","mcp_get_code":{"code_sha256":"04a79674a945ae82"}},{"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_6.2/Rice_HR/Rice_HR.py","file_url":"https://github.com/ryanlucas3/hr_neural_networks/blob/HEAD/HR_Neural_Networks/Paper_experiments/Section_6.2/Rice_HR/Rice_HR.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d1360df29c61c25","mcp_get_code":{"code_sha256":"8d1360df29c61c25"}},{"arxiv_id":"2301.00772","paper":"/paper/pcrlv2-a-unified-visual-information","title":"PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RL4M/PCRLv2","path":"train_2d.py","file_url":"https://github.com/RL4M/PCRLv2/blob/HEAD/train_2d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27b5c00cc9d84e47","mcp_get_code":{"code_sha256":"27b5c00cc9d84e47"}},{"arxiv_id":"2210.03543","paper":"/paper/a2-efficient-automated-attacker-for-boosting","title":"A2: Efficient Automated Attacker for Boosting Adversarial Training","date":"2022-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alipay/A2-efficient-automated-attacker-for-boosting-adversarial-training","path":"train_cifar10.py","file_url":"https://github.com/alipay/A2-efficient-automated-attacker-for-boosting-adversarial-training/blob/HEAD/train_cifar10.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":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2210.00266","paper":"/paper/long-tailed-class-incremental-learning","title":"Long-Tailed Class Incremental Learning","date":"2022-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xialeiliu/Long-Tailed-CIL","path":"src/approach/LAS_utils.py","file_url":"https://github.com/xialeiliu/Long-Tailed-CIL/blob/HEAD/src/approach/LAS_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2209.09476","paper":"/paper/sparcl-sparse-continual-learning-on-the-edge","title":"SparCL: Sparse Continual Learning on the Edge","date":"2022-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"bd19a75b8bed0114","mcp_get_code":{"code_sha256":"bd19a75b8bed0114"}},{"arxiv_id":"2207.13378","paper":"/paper/identifying-hard-noise-in-long-tailed-sample","title":"Identifying Hard Noise in Long-Tailed Sample Distribution","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yxymessi/H2E-Framework","path":"eccv_github/noise_longtail/code/utils.py","file_url":"https://github.com/yxymessi/H2E-Framework/blob/HEAD/eccv_github/noise_longtail/code/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd81207449c239dd","mcp_get_code":{"code_sha256":"fd81207449c239dd"}},{"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":"augmentation/Mixup.py","file_url":"https://github.com/cychomatica/one-pixel-shotcut/blob/HEAD/augmentation/Mixup.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8897714b1e9b6396","mcp_get_code":{"code_sha256":"8897714b1e9b6396"}},{"arxiv_id":"2204.05044","paper":"/paper/from-cnns-to-vision-transformers-a","title":"From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology","date":"2022-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hhi-aml/histobenchmark","path":"code/main_patho_lightning.py","file_url":"https://github.com/hhi-aml/histobenchmark/blob/HEAD/code/main_patho_lightning.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c0a4cf940a467481","mcp_get_code":{"code_sha256":"c0a4cf940a467481"}},{"arxiv_id":"2204.04677","paper":"/paper/fedcorr-multi-stage-federated-learning-for","title":"FedCorr: Multi-Stage Federated Learning for Label Noise Correction","date":"2022-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8897714b1e9b6396","mcp_get_code":{"code_sha256":"8897714b1e9b6396"}},{"arxiv_id":"2202.02471","paper":"/paper/few-shot-learning-as-cluster-induced-voronoi","title":"Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach","date":"2022-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"horsepurve/deepvoro","path":"wrn_mixup_model.py","file_url":"https://github.com/horsepurve/deepvoro/blob/HEAD/wrn_mixup_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b53e6443d8a7fbf3","mcp_get_code":{"code_sha256":"b53e6443d8a7fbf3"}},{"arxiv_id":"2110.14032","paper":"/paper/mest-accurate-and-fast-memory-economic-sparse","title":"MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boone891214/MEST","path":"main_sparse_train.py","file_url":"https://github.com/boone891214/MEST/blob/HEAD/main_sparse_train.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd19a75b8bed0114","mcp_get_code":{"code_sha256":"bd19a75b8bed0114"}},{"arxiv_id":"2107.00166","paper":"/paper/sanity-checks-for-lottery-tickets-does-your","title":"Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boone891214/sanity-check-LTH","path":"cifar/main_prune_train.py","file_url":"https://github.com/boone891214/sanity-check-LTH/blob/HEAD/cifar/main_prune_train.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd81207449c239dd","mcp_get_code":{"code_sha256":"fd81207449c239dd"}},{"arxiv_id":"2106.01342","paper":"/paper/saint-improved-neural-networks-for-tabular","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"somepago/saint","path":"augmentations.py","file_url":"https://github.com/somepago/saint/blob/HEAD/augmentations.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":"3f8482bac2c6b9db","mcp_get_code":{"code_sha256":"3f8482bac2c6b9db"}},{"arxiv_id":"2104.00466","paper":"/paper/improving-calibration-for-long-tailed-1","title":"Improving Calibration for Long-Tailed Recognition","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jia-Research-Lab/MiSLAS","path":"methods.py","file_url":"https://github.com/Jia-Research-Lab/MiSLAS/blob/HEAD/methods.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2102.13280","paper":"/paper/mixsearch-searching-for-domain-generalized","title":"MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures","date":"2021-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"2102.07861","paper":"/paper/low-curvature-activations-reduce-overfitting","title":"Low Curvature Activations Reduce Overfitting in Adversarial Training","date":"2021-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2101.10633","paper":"/paper/reslt-residual-learning-for-long-tailed","title":"ResLT: Residual Learning for Long-tailed Recognition","date":"2021-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiequancui/ResLT","path":"CIFAR/cifarTrain_reslt_cifar10.py","file_url":"https://github.com/jiequancui/ResLT/blob/HEAD/CIFAR/cifarTrain_reslt_cifar10.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"760098c40816bbe8","mcp_get_code":{"code_sha256":"760098c40816bbe8"}},{"arxiv_id":"2101.06395","paper":"/paper/free-lunch-for-few-shot-learning-distribution-1","title":"Free Lunch for Few-shot Learning: Distribution Calibration","date":"2021-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sabagh1994/code_dcf","path":"wrn_model.py","file_url":"https://github.com/sabagh1994/code_dcf/blob/HEAD/wrn_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"56ca7839c3f149ad","mcp_get_code":{"code_sha256":"56ca7839c3f149ad"}},{"arxiv_id":"2010.08887","paper":"/paper/i-mix-a-strategy-for-regularizing-contrastive-1","title":"i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning","date":"2020-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PaulAlbert31/iMix","path":"utils.py","file_url":"https://github.com/PaulAlbert31/iMix/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d289cf4cd8e95ada","mcp_get_code":{"code_sha256":"d289cf4cd8e95ada"}},{"arxiv_id":"2010.07092","paper":"/paper/data-augmentation-for-meta-learning-1","title":"Data Augmentation for Meta-Learning","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"renkunni/metaaug","path":"train_aug.py","file_url":"https://github.com/renkunni/metaaug/blob/HEAD/train_aug.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce1ad25b0d64fdd7","mcp_get_code":{"code_sha256":"ce1ad25b0d64fdd7"}},{"arxiv_id":"2010.07092","paper":"/paper/data-augmentation-for-meta-learning-1","title":"Data Augmentation for Meta-Learning","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RenkunNi/MetaAug","path":"models/R2D2_embedding_mixup.py","file_url":"https://github.com/RenkunNi/MetaAug/blob/HEAD/models/R2D2_embedding_mixup.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fcec7c373958ae2d","mcp_get_code":{"code_sha256":"fcec7c373958ae2d"}},{"arxiv_id":"2010.03558","paper":"/paper/high-capacity-expert-binary-networks-1","title":"High-Capacity Expert Binary Networks","date":"2020-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"1adrianb/expert-binary-networks","path":"utils/mixup.py","file_url":"https://github.com/1adrianb/expert-binary-networks/blob/HEAD/utils/mixup.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aafeecd1181e3b50","mcp_get_code":{"code_sha256":"aafeecd1181e3b50"}},{"arxiv_id":"2010.00467","paper":"/paper/bag-of-tricks-for-adversarial-training","title":"Bag of Tricks for Adversarial Training","date":"2020-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2007.07423","paper":"/paper/comparing-to-learn-surpassing-imagenet","title":"Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations","date":"2020-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"funnyzhou/C2L_MICCAI2020","path":"train_C2L_res18.py","file_url":"https://github.com/funnyzhou/C2L_MICCAI2020/blob/HEAD/train_C2L_res18.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"572eb5c34ec5ef0c","mcp_get_code":{"code_sha256":"572eb5c34ec5ef0c"}},{"arxiv_id":"2004.05884","paper":"/paper/revisiting-loss-landscape-for-adversarial","title":"Adversarial Weight Perturbation Helps Robust Generalization","date":"2020-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csdongxian/AWP","path":"AT_AWP/train_cifar10.py","file_url":"https://github.com/csdongxian/AWP/blob/HEAD/AT_AWP/train_cifar10.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2003.01690","paper":"/paper/reliable-evaluation-of-adversarial-robustness","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","date":"2020-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/robust_overfitting","path":"train_cifar.py","file_url":"https://github.com/locuslab/robust_overfitting/blob/HEAD/train_cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2002.11569","paper":"/paper/overfitting-in-adversarially-robust-deep","title":"Overfitting in adversarially robust deep learning","date":"2020-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"2002.06815","paper":"/paper/class-imbalanced-semi-supervised-learning","title":"Class-Imbalanced Semi-Supervised Learning","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MinsungHyun/Class-Imbalanced-Semi-Supervised-Learning","path":"CISSL_cls/lib/utils.py","file_url":"https://github.com/MinsungHyun/Class-Imbalanced-Semi-Supervised-Learning/blob/HEAD/CISSL_cls/lib/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b062bea460457feb","mcp_get_code":{"code_sha256":"b062bea460457feb"}},{"arxiv_id":"1908.09699","paper":"/paper/gated-convolutional-networks-with-hybrid","title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","date":"2019-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"winycg/HCGNet","path":"main_cifar.py","file_url":"https://github.com/winycg/HCGNet/blob/HEAD/main_cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8897714b1e9b6396","mcp_get_code":{"code_sha256":"8897714b1e9b6396"}},{"arxiv_id":"1908.09699","paper":"/paper/gated-convolutional-networks-with-hybrid","title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","date":"2019-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"winycg/HCGNet","path":"main_imagenet.py","file_url":"https://github.com/winycg/HCGNet/blob/HEAD/main_imagenet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3d04404ba40305cc","mcp_get_code":{"code_sha256":"3d04404ba40305cc"}},{"arxiv_id":"1908.02983","paper":"/paper/pseudo-labeling-and-confirmation-bias-in-deep","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","date":"2019-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricArazo/PseudoLabeling","path":"utils_pseudoLab/utils_ssl.py","file_url":"https://github.com/EricArazo/PseudoLabeling/blob/HEAD/utils_pseudoLab/utils_ssl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3547e83bd03591ad","mcp_get_code":{"code_sha256":"3547e83bd03591ad"}},{"arxiv_id":"1906.06784","paper":"/paper/interpolated-adversarial-training-achieving","title":"Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy","date":"2019-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"11b0ec76b88d8553","mcp_get_code":{"code_sha256":"11b0ec76b88d8553"}},{"arxiv_id":"1905.02175","paper":"/paper/adversarial-examples-are-not-bugs-they-are","title":"Adversarial Examples Are Not Bugs, They Are Features","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ndb796/pytorch-adversarial-training-cifar","path":"interpolated_adversarial_training.py","file_url":"https://github.com/ndb796/pytorch-adversarial-training-cifar/blob/HEAD/interpolated_adversarial_training.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"11b0ec76b88d8553","mcp_get_code":{"code_sha256":"11b0ec76b88d8553"}},{"arxiv_id":"1710.09412","paper":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongyi-zhang/mixup","path":"cifar/utils.py","file_url":"https://github.com/hongyi-zhang/mixup/blob/HEAD/cifar/utils.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":"c8f7fd47a7acbaa3","mcp_get_code":{"code_sha256":"c8f7fd47a7acbaa3"}},{"arxiv_id":"1710.09412","paper":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smilelab-fl/fednoisy","path":"fednoisy/utils/mixup.py","file_url":"https://github.com/smilelab-fl/fednoisy/blob/HEAD/fednoisy/utils/mixup.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1905009bf59716f6","mcp_get_code":{"code_sha256":"1905009bf59716f6"}},{"arxiv_id":"1710.09412","paper":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ryoo72/dimension-wise_mixup","path":"models/utils.py","file_url":"https://github.com/Ryoo72/dimension-wise_mixup/blob/HEAD/models/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b062bea460457feb","mcp_get_code":{"code_sha256":"b062bea460457feb"}},{"arxiv_id":"1710.09412","paper":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"andychinka/dcase-challenge","path":"asc/data_aug.py","file_url":"https://github.com/andychinka/dcase-challenge/blob/HEAD/asc/data_aug.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ef20d27ae01e4c3","mcp_get_code":{"code_sha256":"4ef20d27ae01e4c3"}},{"arxiv_id":"1710.09412","paper":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hazelsuko07/InstaHide","path":"train_cross.py","file_url":"https://github.com/Hazelsuko07/InstaHide/blob/HEAD/train_cross.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7229d6a2824b389","mcp_get_code":{"code_sha256":"e7229d6a2824b389"}},{"arxiv_id":"1706.06083","paper":"/paper/towards-deep-learning-models-resistant-to","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","date":"2017-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"b20f1357b1d8dbf8","mcp_get_code":{"code_sha256":"b20f1357b1d8dbf8"}},{"arxiv_id":"1706.06083","paper":"/paper/towards-deep-learning-models-resistant-to","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","date":"2017-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"11b0ec76b88d8553","mcp_get_code":{"code_sha256":"11b0ec76b88d8553"}},{"arxiv_id":"1702.05464","paper":"/paper/adversarial-discriminative-domain-adaptation","title":"Adversarial Discriminative Domain Adaptation","date":"2017-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Backdrop9019/adda_pytorch-pseudo-mixup-","path":"core/adapt.py","file_url":"https://github.com/Backdrop9019/adda_pytorch-pseudo-mixup-/blob/HEAD/core/adapt.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"084dfb3d87216615","mcp_get_code":{"code_sha256":"084dfb3d87216615"}},{"arxiv_id":"openreview_GNrBEnHPPv","paper":null,"title":"arXiv:openreview_GNrBEnHPPv","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Gang-ww/ABFnet","path":"utils.py","file_url":"https://github.com/Gang-ww/ABFnet/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d9c095b213729e09","mcp_get_code":{"code_sha256":"d9c095b213729e09"}},{"arxiv_id":"aaai_29262","paper":null,"title":"arXiv:aaai_29262","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Keke921/H2T","path":"methods.py","file_url":"https://github.com/Keke921/H2T/blob/HEAD/methods.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}},{"arxiv_id":"aaai_16993","paper":null,"title":"arXiv:aaai_16993","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MJ1021/kcm-code","path":"KCM_implementation_01152021/Binary/utils.py","file_url":"https://github.com/MJ1021/kcm-code/blob/HEAD/KCM_implementation_01152021/Binary/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"de0013b0e10f3103","mcp_get_code":{"code_sha256":"de0013b0e10f3103"}},{"arxiv_id":"Lazarou_Iterative_Label_Cleaning_for_Transductive_and_Semi-Supervised_Few-Shot_Learning_ICCV_2021_paper","paper":null,"title":"arXiv:Lazarou_Iterative_Label_Cleaning_for_Transductive_and_Semi-Supervised_Few-Shot_Learning_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MichalisLazarou/iLPC","path":"wrn_mixup_model.py","file_url":"https://github.com/MichalisLazarou/iLPC/blob/HEAD/wrn_mixup_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b53e6443d8a7fbf3","mcp_get_code":{"code_sha256":"b53e6443d8a7fbf3"}},{"arxiv_id":"136850478","paper":null,"title":"arXiv:136850478","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"leo-gb/UMA","path":"ccs_training/models/mixup.py","file_url":"https://github.com/leo-gb/UMA/blob/HEAD/ccs_training/models/mixup.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"345e624f9cc2e32a","mcp_get_code":{"code_sha256":"345e624f9cc2e32a"}}]}