{"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-criterion","entry":"mixup_criterion","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":28,"n_papers_ran":24,"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":13,"n_samples_ran":10,"n_samples_fingerprinted":0,"n_places":28,"n_places_pointer_only":15,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":7,"ran_fixture":1,"ran":2,"unverified":3},"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":"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":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"arxiv_id":"2402.04663","paper":"/paper/clif-complementary-leaky-integrate-and-fire","title":"CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huuyulong/complementary-lif","path":"utils/data_loaders.py","file_url":"https://github.com/huuyulong/complementary-lif/blob/HEAD/utils/data_loaders.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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":"9b48dc4a035b2412","mcp_get_code":{"code_sha256":"9b48dc4a035b2412"}},{"arxiv_id":"2308.11957","paper":"/paper/ced-consistent-ensemble-distillation-for","title":"CED: Consistent ensemble distillation for audio tagging","date":null,"month_inferred_from_arxiv_id":"2023-08","title_source":"archive","repo":"richermans/ced","path":"utils.py","file_url":"https://github.com/richermans/ced/blob/HEAD/utils.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":"f79c5ee46dcd36be","mcp_get_code":{"code_sha256":"f79c5ee46dcd36be"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed47bc4cea90b34","mcp_get_code":{"code_sha256":"aed47bc4cea90b34"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0016935c9f13abf7","mcp_get_code":{"code_sha256":"0016935c9f13abf7"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"12031ff7668a8502","mcp_get_code":{"code_sha256":"12031ff7668a8502"}},{"arxiv_id":"2209.08928","paper":"/paper/umix-improving-importance-weighting-for","title":"UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup","date":"2022-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencentailabhealthcare/umix","path":"examples/algorithms/UMIX.py","file_url":"https://github.com/tencentailabhealthcare/umix/blob/HEAD/examples/algorithms/UMIX.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":"8e5d10cb937575b9","mcp_get_code":{"code_sha256":"8e5d10cb937575b9"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79edc1957c1e3747","mcp_get_code":{"code_sha256":"79edc1957c1e3747"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"arxiv_id":"2112.06274","paper":"/paper/sparsefed-mitigating-model-poisoning-attacks","title":"SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification","date":"2021-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sparsefed/sparsefed","path":"CommEfficient/cv_train.py","file_url":"https://github.com/sparsefed/sparsefed/blob/HEAD/CommEfficient/cv_train.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62225dd8920c913d","mcp_get_code":{"code_sha256":"62225dd8920c913d"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12031ff7668a8502","mcp_get_code":{"code_sha256":"12031ff7668a8502"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b2fdcdcc7de945a","mcp_get_code":{"code_sha256":"9b2fdcdcc7de945a"}},{"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":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5825603a0ae4a323","mcp_get_code":{"code_sha256":"5825603a0ae4a323"}},{"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":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79edc1957c1e3747","mcp_get_code":{"code_sha256":"79edc1957c1e3747"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"33ba52fc17e89516","mcp_get_code":{"code_sha256":"33ba52fc17e89516"}},{"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":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"33ba52fc17e89516","mcp_get_code":{"code_sha256":"33ba52fc17e89516"}},{"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":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33ba52fc17e89516","mcp_get_code":{"code_sha256":"33ba52fc17e89516"}},{"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":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"33ba52fc17e89516","mcp_get_code":{"code_sha256":"33ba52fc17e89516"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed47bc4cea90b34","mcp_get_code":{"code_sha256":"aed47bc4cea90b34"}},{"arxiv_id":"2021.emnlp-main.154","paper":null,"title":"arXiv:2021.emnlp-main.154","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hazelsuko07/TextHide","path":"instahide.py","file_url":"https://github.com/Hazelsuko07/TextHide/blob/HEAD/instahide.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6588875b2a3f66d1","mcp_get_code":{"code_sha256":"6588875b2a3f66d1"}},{"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_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"97f484048ac03556","mcp_get_code":{"code_sha256":"97f484048ac03556"}}]}