{"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/balanced-softmax-loss","entry":"balanced_softmax_loss","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":9,"n_papers_ran":6,"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":9,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":3,"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":"2407.16802","paper":"/paper/distribution-aware-robust-learning-from-long","title":"Distribution-Aware Robust Learning from Long-Tailed Data with Noisy Labels","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JaesoonBaik1213/DaSC","path":"experiment/train_cifar_ssl.py","file_url":"https://github.com/JaesoonBaik1213/DaSC/blob/HEAD/experiment/train_cifar_ssl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd714bbda15c3c95","mcp_get_code":{"code_sha256":"fd714bbda15c3c95"}},{"arxiv_id":"2406.04596","paper":"/paper/federated-representation-learning-in-the","title":"Federated Representation Learning in the Under-Parameterized Regime","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RenpuLiu/flute","path":"models/bsml.py","file_url":"https://github.com/RenpuLiu/flute/blob/HEAD/models/bsml.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6d3bab23c40891a","mcp_get_code":{"code_sha256":"c6d3bab23c40891a"}},{"arxiv_id":"2406.04596","paper":"/paper/federated-representation-learning-in-the","title":"Federated Representation Learning in the Under-Parameterized Regime","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RenpuLiu/flute","path":"utils/bsml.py","file_url":"https://github.com/RenpuLiu/flute/blob/HEAD/utils/bsml.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9dc354a7407a75b8","mcp_get_code":{"code_sha256":"9dc354a7407a75b8"}},{"arxiv_id":"2405.11756","paper":"/paper/erasing-the-bias-fine-tuning-foundation","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gank0078/FineSSL","path":"utils/losses.py","file_url":"https://github.com/Gank0078/FineSSL/blob/HEAD/utils/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec713fa2011796da","mcp_get_code":{"code_sha256":"ec713fa2011796da"}},{"arxiv_id":"2307.08235","paper":"/paper/herolt-benchmarking-heterogeneous-long-tailed","title":"Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SSSKJ/HeroLT","path":"HeroLT/nn/Loss/BalancedSoftmaxLoss.py","file_url":"https://github.com/SSSKJ/HeroLT/blob/HEAD/HeroLT/nn/Loss/BalancedSoftmaxLoss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9dc354a7407a75b8","mcp_get_code":{"code_sha256":"9dc354a7407a75b8"}},{"arxiv_id":"2303.10058","paper":"/paper/no-fear-of-classifier-biases-neural-collapse","title":"No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zexilee/iccv-2023-fedetf","path":"client_funct.py","file_url":"https://github.com/zexilee/iccv-2023-fedetf/blob/HEAD/client_funct.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":"a0ad76ccb9701aa3","mcp_get_code":{"code_sha256":"a0ad76ccb9701aa3"}},{"arxiv_id":"2211.01572","paper":"/paper/fedtp-federated-learning-by-transformer","title":"FedTP: Federated Learning by Transformer Personalization","date":"2022-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhyczy/fedtp","path":"methods/method.py","file_url":"https://github.com/zhyczy/fedtp/blob/HEAD/methods/method.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ba78df1e37b2c77","mcp_get_code":{"code_sha256":"4ba78df1e37b2c77"}},{"arxiv_id":"2107.00778","paper":"/paper/on-bridging-generic-and-personalized","title":"On Bridging Generic and Personalized Federated Learning for Image Classification","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TsingZ0/PFL-Non-IID","path":"system/flcore/clients/clientrod.py","file_url":"https://github.com/TsingZ0/PFL-Non-IID/blob/HEAD/system/flcore/clients/clientrod.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a68ecf850048b341","mcp_get_code":{"code_sha256":"a68ecf850048b341"}},{"arxiv_id":"2007.10740","paper":"/paper/balanced-meta-softmax-for-long-tailed-visual","title":"Balanced Meta-Softmax for Long-Tailed Visual Recognition","date":"2020-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiawei-ren/BalancedMetaSoftmax-Classification","path":"loss/BalancedSoftmaxLoss.py","file_url":"https://github.com/jiawei-ren/BalancedMetaSoftmax-Classification/blob/HEAD/loss/BalancedSoftmaxLoss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d3cf8659227d9fa6","mcp_get_code":{"code_sha256":"d3cf8659227d9fa6"}},{"arxiv_id":"ijcai2023_0434","paper":null,"title":"arXiv:ijcai2023_0434","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HotanLee/SFA","path":"Train_webvision.py","file_url":"https://github.com/HotanLee/SFA/blob/HEAD/Train_webvision.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cab419e4f6f80c1f","mcp_get_code":{"code_sha256":"cab419e4f6f80c1f"}}]}