{"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/rand-bbox","entry":"rand_bbox","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":63,"n_papers_ran":33,"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":30,"n_samples_ran":13,"n_samples_fingerprinted":4,"n_places":67,"n_places_pointer_only":24,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":1,"ran":9,"unverified":17},"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":"2608.16010","paper":"/paper/arxiv-2608-16010","title":"Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"EnumaCaliber/BRIDGE","path":"pretrain_transformer_tiny_imagenet.py","file_url":"https://github.com/EnumaCaliber/BRIDGE/blob/HEAD/pretrain_transformer_tiny_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd2e20a9029caafe","mcp_get_code":{"code_sha256":"bd2e20a9029caafe"}},{"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":"relabel/utils_fkd.py","file_url":"https://github.com/Jiacheng8/FADRM/blob/HEAD/relabel/utils_fkd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"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/1-basic_prompt/best_solution.py","file_url":"https://github.com/anna-richter/be-fair/blob/HEAD/aide/logs/1-basic_prompt/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":"6b4cb20b431b8d39","mcp_get_code":{"code_sha256":"6b4cb20b431b8d39"}},{"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/lisa.py","file_url":"https://github.com/zxcvfd13502/TEA/blob/HEAD/methods/lisa.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"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":"relabel/utils_fkd.py","file_url":"https://github.com/jiacheng8/fadrm/blob/HEAD/relabel/utils_fkd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"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":"relabel/utils_fkd.py","file_url":"https://github.com/jiacheng8/cv-dd/blob/HEAD/relabel/utils_fkd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2411.14429","paper":"/paper/revisiting-the-integration-of-convolution-and","title":"Revisiting the Integration of Convolution and Attention for Vision Backbone","date":"2024-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rayleizhu/GLMix","path":"models/glnet_tklb.py","file_url":"https://github.com/rayleizhu/GLMix/blob/HEAD/models/glnet_tklb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd61d1e4d25b3c45","mcp_get_code":{"code_sha256":"bd61d1e4d25b3c45"}},{"arxiv_id":"2409.18055","paper":"/paper/visual-data-diagnosis-and-debiasing-with","title":"Visual Data Diagnosis and Debiasing with Concept Graphs","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rwchakra/conbias","path":"src/cutmix_proc.py","file_url":"https://github.com/rwchakra/conbias/blob/HEAD/src/cutmix_proc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2407.15138","paper":"/paper/d-4-m-dataset-distillation-via-disentangled","title":"D$^4$M: Dataset Distillation via Disentangled Diffusion Model","date":"2024-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"richards94/D4M","path":"matching/utils_fkd.py","file_url":"https://github.com/richards94/D4M/blob/HEAD/matching/utils_fkd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2404.01705","paper":"/paper/samba-semantic-segmentation-of-remotely","title":"Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuqinfeng1999/samba","path":"mmseg/models/backbones/Samba.py","file_url":"https://github.com/zhuqinfeng1999/samba/blob/HEAD/mmseg/models/backbones/Samba.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":"d84653ce829572b8","mcp_get_code":{"code_sha256":"d84653ce829572b8"}},{"arxiv_id":"2403.18063","paper":"/paper/spectral-convolutional-transformer","title":"Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"badripatro/heracles","path":"heracles.py","file_url":"https://github.com/badripatro/heracles/blob/HEAD/heracles.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d84653ce829572b8","mcp_get_code":{"code_sha256":"d84653ce829572b8"}},{"arxiv_id":"2403.15360","paper":"/paper/simba-simplified-mamba-based-architecture-for","title":"SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series","date":"2024-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"badripatro/simba","path":"classification/simba.py","file_url":"https://github.com/badripatro/simba/blob/HEAD/classification/simba.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d84653ce829572b8","mcp_get_code":{"code_sha256":"d84653ce829572b8"}},{"arxiv_id":"2403.09977","paper":"/paper/efficientvmamba-atrous-selective-scan-for","title":"EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"terrypei/efficientvmamba","path":"classification/lib/dataset/mixup.py","file_url":"https://github.com/terrypei/efficientvmamba/blob/HEAD/classification/lib/dataset/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2402.14015","paper":"/paper/corrective-machine-unlearning","title":"Corrective Machine Unlearning","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"drimpossible/corrective-unlearning-bench","path":"src/utils.py","file_url":"https://github.com/drimpossible/corrective-unlearning-bench/blob/HEAD/src/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":"2e865549db6f7e9c","mcp_get_code":{"code_sha256":"2e865549db6f7e9c"}},{"arxiv_id":"2402.11301","paper":"/paper/revit-enhancing-vision-transformers-with","title":"ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adiko1997/revit","path":"dataset/mixup.py","file_url":"https://github.com/adiko1997/revit/blob/HEAD/dataset/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d66bc560c9e0685","mcp_get_code":{"code_sha256":"1d66bc560c9e0685"}},{"arxiv_id":"2401.09417","paper":"/paper/vision-mamba-efficient-visual-representation","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2401.03497","paper":"/paper/eat-self-supervised-pre-training-with","title":"EAT: Self-Supervised Pre-Training with Efficient Audio Transformer","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwx-worst-one/eat","path":"utils/mixup.py","file_url":"https://github.com/cwx-worst-one/eat/blob/HEAD/utils/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2312.11954","paper":"/paper/adversarial-automixup","title":"Adversarial AutoMixup","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JinXins/Adversarial-AutoMixup","path":"openmixup/models/augments/snapmix.py","file_url":"https://github.com/JinXins/Adversarial-AutoMixup/blob/HEAD/openmixup/models/augments/snapmix.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":"994d60e6d1a8bdb8","mcp_get_code":{"code_sha256":"994d60e6d1a8bdb8"}},{"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":"stage1.py","file_url":"https://github.com/stomach-ache/long-tailed-ood-detection/blob/HEAD/stage1.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dc38cf45af8c7880","mcp_get_code":{"code_sha256":"dc38cf45af8c7880"}},{"arxiv_id":"2311.03747","paper":"/paper/sbcformer-lightweight-network-capable-of-full","title":"SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyonglu/sbcformer","path":"mixup.py","file_url":"https://github.com/xyonglu/sbcformer/blob/HEAD/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"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":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"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":"utils.py","file_url":"https://github.com/bartwojcik/d2dmoe/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2309.13415","paper":"/paper/dream-the-impossible-outlier-imagination-with-1","title":"Dream the Impossible: Outlier Imagination with Diffusion Models","date":"2023-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deeplearning-wisc/dream-ood","path":"scripts/train_gene_in100.py","file_url":"https://github.com/deeplearning-wisc/dream-ood/blob/HEAD/scripts/train_gene_in100.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2309.11523","paper":"/paper/rmt-retentive-networks-meet-vision","title":"RMT: Retentive Networks Meet Vision Transformers","date":"2023-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qhfan/RMT","path":"classification_token_label_release/RMT.py","file_url":"https://github.com/qhfan/RMT/blob/HEAD/classification_token_label_release/RMT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c1ed109d6899c9a6","mcp_get_code":{"code_sha256":"c1ed109d6899c9a6"}},{"arxiv_id":"2308.06582","paper":"/paper/gated-attention-coding-for-training-high","title":"Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks","date":"2023-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bollossom/GAC","path":"CODE/ImageNet/train_amp.py","file_url":"https://github.com/bollossom/GAC/blob/HEAD/CODE/ImageNet/train_amp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc20496120a56972","mcp_get_code":{"code_sha256":"fc20496120a56972"}},{"arxiv_id":"2308.04549","paper":"/paper/prune-spatio-temporal-tokens-by-semantic","title":"Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mark12ding/sta","path":"mixup.py","file_url":"https://github.com/mark12ding/sta/blob/HEAD/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2307.02227","paper":"/paper/mae-dfer-efficient-masked-autoencoder-for","title":"MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition","date":"2023-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunlicai/mae-dfer","path":"mixup.py","file_url":"https://github.com/sunlicai/mae-dfer/blob/HEAD/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2306.11911","paper":"/paper/lnl-k-learning-with-noisy-labels-and-noise","title":"LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration","date":"2023-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunnysiqi/lnl_k","path":"cell_data/simple_multi_main.py","file_url":"https://github.com/sunnysiqi/lnl_k/blob/HEAD/cell_data/simple_multi_main.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c63a96489175258f","mcp_get_code":{"code_sha256":"c63a96489175258f"}},{"arxiv_id":"2306.07703","paper":"/paper/e2e-load-end-to-end-long-form-online-action","title":"E2E-LOAD: End-to-End Long-form Online Action Detection","date":"2023-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sqiangcao99/e2e-load","path":"src/datasets/clipmix.py","file_url":"https://github.com/sqiangcao99/e2e-load/blob/HEAD/src/datasets/clipmix.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":"d0250f7370065e10","mcp_get_code":{"code_sha256":"d0250f7370065e10"}},{"arxiv_id":"2306.02368","paper":"/paper/revisiting-data-free-knowledge-distillation","title":"Revisiting Data-Free Knowledge Distillation with Poisoned Teachers","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"illidanlab/abd","path":"cmi/vanilla_kd_ood.py","file_url":"https://github.com/illidanlab/abd/blob/HEAD/cmi/vanilla_kd_ood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2305.19254","paper":"/paper/what-can-we-learn-from-unlearnable-datasets-1","title":"What Can We Learn from Unlearnable Datasets?","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"psandovalsegura/learn-from-unlearnable","path":"orthogonal_projection_step_2.py","file_url":"https://github.com/psandovalsegura/learn-from-unlearnable/blob/HEAD/orthogonal_projection_step_2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2305.08661","paper":"/paper/global-and-local-mixture-consistency-1","title":"Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions","date":"2023-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ynu-yangpeng/GLMC","path":"GLMC-2023/Trainer.py","file_url":"https://github.com/ynu-yangpeng/GLMC/blob/HEAD/GLMC-2023/Trainer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94a1b38875494387","mcp_get_code":{"code_sha256":"94a1b38875494387"}},{"arxiv_id":"2305.02507","paper":"/paper/stimulative-training-go-beyond-the","title":"Stimulative Training++: Go Beyond The Performance Limits of Residual Networks","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshine-ye/nips22-st","path":"mixup.py","file_url":"https://github.com/sunshine-ye/nips22-st/blob/HEAD/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2304.10716","paper":"/paper/joint-token-pruning-and-squeezing-towards","title":"Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers","date":"2023-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/TPS-CVPR2023","path":"megengine_codebase/mixup.py","file_url":"https://github.com/megvii-research/TPS-CVPR2023/blob/HEAD/megengine_codebase/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":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"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":"ISS_augs.py","file_url":"https://github.com/hmdolatabadi/avatar/blob/HEAD/ISS_augs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3d09a4dd10e16e3","mcp_get_code":{"code_sha256":"b3d09a4dd10e16e3"}},{"arxiv_id":"2302.04869","paper":"/paper/reversible-vision-transformers-1","title":"Reversible Vision Transformers","date":"2023-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/mvit","path":"mvit/datasets/mixup.py","file_url":"https://github.com/facebookresearch/mvit/blob/HEAD/mvit/datasets/mixup.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":"f18f66adc8c6b70f","mcp_get_code":{"code_sha256":"f18f66adc8c6b70f"}},{"arxiv_id":"2301.03580","paper":"/paper/designing-bert-for-convolutional-networks","title":"Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling","date":"2023-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keyu-tian/spark","path":"downstream_imagenet/mixup.py","file_url":"https://github.com/keyu-tian/spark/blob/HEAD/downstream_imagenet/mixup.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2212.00776","paper":"/paper/resformer-scaling-vits-with-multi-resolution","title":"ResFormer: Scaling ViTs with Multi-Resolution Training","date":"2022-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruitian12/resformer","path":"image_classification/mix.py","file_url":"https://github.com/ruitian12/resformer/blob/HEAD/image_classification/mix.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2207.04978","paper":"/paper/wave-vit-unifying-wavelet-and-transformers","title":"Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning","date":"2022-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YehLi/ImageNetModel","path":"classification/dualvit.py","file_url":"https://github.com/YehLi/ImageNetModel/blob/HEAD/classification/dualvit.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":"2ce47798c493fe3f","mcp_get_code":{"code_sha256":"2ce47798c493fe3f"}},{"arxiv_id":"2206.13559","paper":"/paper/parameter-efficient-image-to-video-transfer","title":"ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning","date":"2022-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linziyi96/st-adapter","path":"video_dataset/mixup.py","file_url":"https://github.com/linziyi96/st-adapter/blob/HEAD/video_dataset/mixup.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"830c7d87b9bb8311","mcp_get_code":{"code_sha256":"830c7d87b9bb8311"}},{"arxiv_id":"2206.07692","paper":"/paper/a-simple-data-mixing-prior-for-improving-self-1","title":"A Simple Data Mixing Prior for Improving Self-Supervised Learning","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OliverRensu/SDMP","path":"moco/moco/builder.py","file_url":"https://github.com/OliverRensu/SDMP/blob/HEAD/moco/moco/builder.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":"17dd404572e11129","mcp_get_code":{"code_sha256":"17dd404572e11129"}},{"arxiv_id":"2206.07662","paper":"/paper/sp-vit-learning-2d-spatial-priors-for-vision","title":"SP-ViT: Learning 2D Spatial Priors for Vision Transformers","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhouYuxuanYX/SP-ViT","path":"tlt/models/spvit.py","file_url":"https://github.com/ZhouYuxuanYX/SP-ViT/blob/HEAD/tlt/models/spvit.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":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2204.00993","paper":"/paper/improving-vision-transformers-by-revisiting","title":"Improving Vision Transformers by Revisiting High-frequency Components","date":"2022-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiawangbai/HAT","path":"models/volo.py","file_url":"https://github.com/jiawangbai/HAT/blob/HEAD/models/volo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b66088e90761117d","mcp_get_code":{"code_sha256":"b66088e90761117d"}},{"arxiv_id":"2203.14509","paper":"/paper/automated-progressive-learning-for-efficient","title":"Automated Progressive Learning for Efficient Training of Vision Transformers","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"changlin31/autoprog","path":"models/volo.py","file_url":"https://github.com/changlin31/autoprog/blob/HEAD/models/volo.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":"b66088e90761117d","mcp_get_code":{"code_sha256":"b66088e90761117d"}},{"arxiv_id":"2203.09744","paper":"/paper/class-balanced-pixel-level-self-labeling-for","title":"Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation","date":"2022-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lslrh/CPSL","path":"models/adaptation_modelv2.py","file_url":"https://github.com/lslrh/CPSL/blob/HEAD/models/adaptation_modelv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8d7e70cf51107f2","mcp_get_code":{"code_sha256":"c8d7e70cf51107f2"}},{"arxiv_id":"2203.06345","paper":"/paper/the-principle-of-diversity-training-stronger","title":"The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy","date":"2022-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/Diverse-ViT","path":"mix.py","file_url":"https://github.com/VITA-Group/Diverse-ViT/blob/HEAD/mix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d0b649ce5448ea07","mcp_get_code":{"code_sha256":"d0b649ce5448ea07"}},{"arxiv_id":"2203.06145","paper":"/paper/neuromorphic-data-augmentation-for-training","title":"Neuromorphic Data Augmentation for Training Spiking Neural Networks","date":"2022-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"intelligent-computing-lab-yale/nda_snn","path":"functions/data_loaders.py","file_url":"https://github.com/intelligent-computing-lab-yale/nda_snn/blob/HEAD/functions/data_loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c94f75b96beb7aef","mcp_get_code":{"code_sha256":"c94f75b96beb7aef"}},{"arxiv_id":"2112.00412","paper":"/paper/the-majority-can-help-the-minority-context","title":"The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"0756a99508caf42c","mcp_get_code":{"code_sha256":"0756a99508caf42c"}},{"arxiv_id":"2112.00412","paper":"/paper/the-majority-can-help-the-minority-context","title":"The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification","date":"2021-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver-ai/cmo","path":"cifar_train.py","file_url":"https://github.com/naver-ai/cmo/blob/HEAD/cifar_train.py","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc38cf45af8c7880","mcp_get_code":{"code_sha256":"dc38cf45af8c7880"}},{"arxiv_id":"2111.09883","paper":"/paper/swin-transformer-v2-scaling-up-capacity-and","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2","path":"mixup.py","file_url":"https://github.com/nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2/blob/HEAD/mixup.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":"484edb411de743ae","mcp_get_code":{"code_sha256":"484edb411de743ae"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/mae","path":"src/datasets/mixup.py","file_url":"https://github.com/yangyucheng000/mae/blob/HEAD/src/datasets/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":"3913c620f6926f42","mcp_get_code":{"code_sha256":"3913c620f6926f42"}},{"arxiv_id":"2109.03508","paper":"/paper/repnas-searching-for-efficient-re","title":"RepNAS: Searching for Efficient Re-parameterizing Blocks","date":"2021-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bestfleer/RepNAS","path":"utils/Mixup.py","file_url":"https://github.com/bestfleer/RepNAS/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":"45e8fbb4a42814eb","mcp_get_code":{"code_sha256":"45e8fbb4a42814eb"}},{"arxiv_id":"2107.02408","paper":"/paper/cored-generalizing-fake-media-detection-with","title":"CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alsgkals2/cored","path":"Function_common.py","file_url":"https://github.com/alsgkals2/cored/blob/HEAD/Function_common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e865549db6f7e9c","mcp_get_code":{"code_sha256":"2e865549db6f7e9c"}},{"arxiv_id":"2107.02408","paper":"/paper/cored-generalizing-fake-media-detection-with","title":"CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation","date":"2021-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alsgkals2/CoReD_Released","path":"Function_common.py","file_url":"https://github.com/alsgkals2/CoReD_Released/blob/HEAD/Function_common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2106.13112","paper":"/paper/volo-vision-outlooker-for-visual-recognition","title":"VOLO: Vision Outlooker for Visual Recognition","date":"2021-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/volo","path":"models/volo.py","file_url":"https://github.com/sail-sg/volo/blob/HEAD/models/volo.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":"b66088e90761117d","mcp_get_code":{"code_sha256":"b66088e90761117d"}},{"arxiv_id":"2106.03714","paper":"/paper/refiner-refining-self-attention-for-vision","title":"Refiner: Refining Self-attention for Vision Transformers","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2104.10858","paper":"/paper/token-labeling-training-a-85-5-top-1-accuracy","title":"All Tokens Matter: Token Labeling for Training Better Vision Transformers","date":"2021-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"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":"augmentations.py","file_url":"https://github.com/RenkunNi/MetaAug/blob/HEAD/augmentations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"23e0895db32c5436","mcp_get_code":{"code_sha256":"23e0895db32c5436"}},{"arxiv_id":"2008.04115","paper":"/paper/t-gd-transferable-gan-generated-images","title":"T-GD: Transferable GAN-generated Images Detection Framework","date":"2020-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cutz-j/T-GD","path":"utils/aug.py","file_url":"https://github.com/cutz-j/T-GD/blob/HEAD/utils/aug.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2007.00992","paper":"/paper/rexnet-diminishing-representational","title":"Rethinking Channel Dimensions for Efficient Model Design","date":"2020-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ysbsb/ReXNet-PyTorch","path":"train_sgd_randaugv1_cutmix.py","file_url":"https://github.com/ysbsb/ReXNet-PyTorch/blob/HEAD/train_sgd_randaugv1_cutmix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2003.05438","paper":"/paper/rethinking-image-mixture-for-unsupervised","title":"Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning","date":"2020-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning","path":"UnMix_MoCo/for_ImageNet/main_moco_unmix.py","file_url":"https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning/blob/HEAD/UnMix_MoCo/for_ImageNet/main_moco_unmix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"2002.11102","paper":"/paper/on-feature-normalization-and-data","title":"On Feature Normalization and Data Augmentation","date":"2020-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Boyiliee/MoEx","path":"CIFAR/train_moex.py","file_url":"https://github.com/Boyiliee/MoEx/blob/HEAD/CIFAR/train_moex.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":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"1905.04899","paper":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}},{"arxiv_id":"1905.04899","paper":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jis478/Tensorflow","path":"TF2.0/Cutmix/Functions.py","file_url":"https://github.com/jis478/Tensorflow/blob/HEAD/TF2.0/Cutmix/Functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5224b4839efbc28","mcp_get_code":{"code_sha256":"c5224b4839efbc28"}},{"arxiv_id":"1905.04899","paper":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ildoonet/cutmix","path":"cutmix/cutmix.py","file_url":"https://github.com/ildoonet/cutmix/blob/HEAD/cutmix/cutmix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3d09a4dd10e16e3","mcp_get_code":{"code_sha256":"b3d09a4dd10e16e3"}},{"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":"facebookresearch/ClassyVision","path":"classy_vision/dataset/transforms/mixup.py","file_url":"https://github.com/facebookresearch/ClassyVision/blob/HEAD/classy_vision/dataset/transforms/mixup.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19ead423fd887adc","mcp_get_code":{"code_sha256":"19ead423fd887adc"}},{"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/cutmix.py","file_url":"https://github.com/leo-gb/UMA/blob/HEAD/ccs_training/models/cutmix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d34a085d71b8f4b5","mcp_get_code":{"code_sha256":"d34a085d71b8f4b5"}}]}