{"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/translate-x-rel","entry":"translate_x_rel","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":27,"n_papers_ran":21,"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":7,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":28,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2605.10047","paper":"/paper/arxiv-2605-10047","title":"Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"tongzixin716716/Inverse-Loss-Reweighting","path":"randaugment.py","file_url":"https://github.com/tongzixin716716/Inverse-Loss-Reweighting/blob/HEAD/randaugment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"arxiv_id":"2410.16038","paper":"/paper/benchmarking-pathology-foundation-models","title":"Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"quiil/benchmarkingpathologyfoundationmodels","path":"data_lib/RandAugment.py","file_url":"https://github.com/quiil/benchmarkingpathologyfoundationmodels/blob/HEAD/data_lib/RandAugment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"arxiv_id":"2408.16266","paper":"/paper/improving-diffusion-based-data-augmentation","title":"Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scuwyh2000/diff-ii","path":"randaugment.py","file_url":"https://github.com/scuwyh2000/diff-ii/blob/HEAD/randaugment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"arxiv_id":"2408.02192","paper":"/paper/2408-02192","title":"Unsupervised Domain Adaption Harnessing Vision-Language Pre-training","date":"2024-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Wenlve-Zhou/VLP-UDA","path":"utils/randaugment.py","file_url":"https://github.com/Wenlve-Zhou/VLP-UDA/blob/HEAD/utils/randaugment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"arxiv_id":"2406.07471","paper":"/paper/ophnet-a-large-scale-video-benchmark-for","title":"OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minghu0830/ophnet-benchmark","path":"baselines/task2/backbone/videomaev2/dataset/rand_augment.py","file_url":"https://github.com/minghu0830/ophnet-benchmark/blob/HEAD/baselines/task2/backbone/videomaev2/dataset/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2403.11138","paper":"/paper/spiking-wavelet-transformer","title":"Spiking Wavelet Transformer","date":"2024-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bic-l/spiking-wavelet-transformer","path":"cifar10-100/aa_snn.py","file_url":"https://github.com/bic-l/spiking-wavelet-transformer/blob/HEAD/cifar10-100/aa_snn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"61f2a3e08a11e1f5","mcp_get_code":{"code_sha256":"61f2a3e08a11e1f5"}},{"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/augment_ops.py","file_url":"https://github.com/terrypei/efficientvmamba/blob/HEAD/classification/lib/dataset/augment_ops.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f38706e32849458d","mcp_get_code":{"code_sha256":"f38706e32849458d"}},{"arxiv_id":"2403.06726","paper":"/paper/probabilistic-contrastive-learning-for-long","title":"Probabilistic Contrastive Learning for Long-Tailed Visual Recognition","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/proco","path":"ProCo/randaugment.py","file_url":"https://github.com/leaplabthu/proco/blob/HEAD/ProCo/randaugment.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":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"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/rand_augment.py","file_url":"https://github.com/adiko1997/revit/blob/HEAD/dataset/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2401.02020","paper":"/paper/spikformer-v2-join-the-high-accuracy-club-on","title":"Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zk-zhou/spikformer","path":"cifar10/aa_snn.py","file_url":"https://github.com/zk-zhou/spikformer/blob/HEAD/cifar10/aa_snn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"61f2a3e08a11e1f5","mcp_get_code":{"code_sha256":"61f2a3e08a11e1f5"}},{"arxiv_id":"2312.05447","paper":"/paper/from-static-to-dynamic-adapting-landmark-1","title":"From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos","date":"2023-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FER-LMC/S2D","path":"datasets/rand_augment.py","file_url":"https://github.com/FER-LMC/S2D/blob/HEAD/datasets/rand_augment.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":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2309.12742","paper":"/paper/make-the-u-in-uda-matter-invariant-1","title":"Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yue-zhongqi/ICON","path":"icon/randaugment.py","file_url":"https://github.com/yue-zhongqi/ICON/blob/HEAD/icon/randaugment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"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":"rand_augment.py","file_url":"https://github.com/mark12ding/sta/blob/HEAD/rand_augment.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":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2307.06947","paper":"/paper/video-focalnets-spatio-temporal-focal","title":"Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition","date":"2023-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"talalwasim/video-focalnets","path":"datasets/rand_augment.py","file_url":"https://github.com/talalwasim/video-focalnets/blob/HEAD/datasets/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"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/rand_augment.py","file_url":"https://github.com/facebookresearch/mvit/blob/HEAD/mvit/datasets/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2212.03640","paper":"/paper/fine-tuned-clip-models-are-efficient-video","title":"Fine-tuned CLIP Models are Efficient Video Learners","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muzairkhattak/vifi-clip","path":"datasets/rand_augment.py","file_url":"https://github.com/muzairkhattak/vifi-clip/blob/HEAD/datasets/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2211.09120","paper":"/paper/adamae-adaptive-masking-for-efficient","title":"AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders","date":"2022-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/adamae","path":"rand_augment.py","file_url":"https://github.com/wgcban/adamae/blob/HEAD/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2207.09176","paper":"/paper/self-supervision-can-be-a-good-few-shot","title":"Self-Supervision Can Be a Good Few-Shot Learner","date":"2022-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbbdylan/unisiam","path":"transform/rand_augmentation.py","file_url":"https://github.com/bbbdylan/unisiam/blob/HEAD/transform/rand_augmentation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"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/rand_augment.py","file_url":"https://github.com/linziyi96/st-adapter/blob/HEAD/video_dataset/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"2203.14415","paper":"/paper/mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/mugs","path":"src/RandAugment.py","file_url":"https://github.com/sail-sg/mugs/blob/HEAD/src/RandAugment.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":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"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/auto_augment.py","file_url":"https://github.com/yangyucheng000/mae/blob/HEAD/src/datasets/auto_augment.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":"f3b161d964735796","mcp_get_code":{"code_sha256":"f3b161d964735796"}},{"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":"yongyupei/papers_with_examps","path":"mae/src/process_datasets/auto_augment.py","file_url":"https://github.com/yongyupei/papers_with_examps/blob/HEAD/mae/src/process_datasets/auto_augment.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":"1644b5d70651bbcf","mcp_get_code":{"code_sha256":"1644b5d70651bbcf"}},{"arxiv_id":"2109.06165","paper":"/paper/cdtrans-cross-domain-transformer-for","title":"CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation","date":"2021-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cdtrans/cdtrans","path":"datasets/autoaugment.py","file_url":"https://github.com/cdtrans/cdtrans/blob/HEAD/datasets/autoaugment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa897f695e05da40","mcp_get_code":{"code_sha256":"aa897f695e05da40"}},{"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/auto_augment.py","file_url":"https://github.com/bestfleer/RepNAS/blob/HEAD/utils/auto_augment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"61f2a3e08a11e1f5","mcp_get_code":{"code_sha256":"61f2a3e08a11e1f5"}},{"arxiv_id":"1811.12814","paper":"/paper/graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyucheng000/glore_res200","path":"src/transform_utils.py","file_url":"https://github.com/yangyucheng000/glore_res200/blob/HEAD/src/transform_utils.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":"f38706e32849458d","mcp_get_code":{"code_sha256":"f38706e32849458d"}},{"arxiv_id":"ijcai2025_0115","paper":null,"title":"arXiv:ijcai2025_0115","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Gwxer/Hierarchical-Adapter","path":"datasets/rand_augment.py","file_url":"https://github.com/Gwxer/Hierarchical-Adapter/blob/HEAD/datasets/rand_augment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"323759f906fbb394","mcp_get_code":{"code_sha256":"323759f906fbb394"}},{"arxiv_id":"136950157","paper":null,"title":"arXiv:136950157","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"kami93/kcenter_video","path":"kcenter_transformer/datasets/autoaugment.py","file_url":"https://github.com/kami93/kcenter_video/blob/HEAD/kcenter_transformer/datasets/autoaugment.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":"a4b471197204b414","mcp_get_code":{"code_sha256":"a4b471197204b414"}},{"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/autoaugment_v2.py","file_url":"https://github.com/leo-gb/UMA/blob/HEAD/ccs_training/models/autoaugment_v2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa897f695e05da40","mcp_get_code":{"code_sha256":"aa897f695e05da40"}}]}