{"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/shot-acc","entry":"shot_acc","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":8,"n_papers_ran":4,"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":6,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"utils.py","file_url":"https://github.com/tongzixin716716/Inverse-Loss-Reweighting/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3612da0362c505e5","mcp_get_code":{"code_sha256":"3612da0362c505e5"}},{"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/utils.py","file_url":"https://github.com/leaplabthu/proco/blob/HEAD/ProCo/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":"3612da0362c505e5","mcp_get_code":{"code_sha256":"3612da0362c505e5"}},{"arxiv_id":"2312.10686","paper":"/paper/out-of-distribution-detection-in-long-tailed","title":"Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning","date":"2023-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mala-lab/cocl","path":"utils/ltr_metrics.py","file_url":"https://github.com/mala-lab/cocl/blob/HEAD/utils/ltr_metrics.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":"a7d912dd30b6673e","mcp_get_code":{"code_sha256":"a7d912dd30b6673e"}},{"arxiv_id":"2304.09426","paper":"/paper/decoupled-training-for-long-tailed","title":"Decoupled Training for Long-Tailed Classification With Stochastic Representations","date":"2023-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhmiao/OpenLongTailRecognition-OLTR","path":"utils.py","file_url":"https://github.com/zhmiao/OpenLongTailRecognition-OLTR/blob/HEAD/utils.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":"ebb474138acbc60c","mcp_get_code":{"code_sha256":"ebb474138acbc60c"}},{"arxiv_id":"2208.02567","paper":"/paper/constructing-balance-from-imbalance-for-long","title":"Constructing Balance from Imbalance for Long-tailed Image Recognition","date":"2022-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"silicx/DLSA","path":"solvers/cluster_aided_classifier.py","file_url":"https://github.com/silicx/DLSA/blob/HEAD/solvers/cluster_aided_classifier.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":"2d1bf3bf736db923","mcp_get_code":{"code_sha256":"2d1bf3bf736db923"}},{"arxiv_id":"2111.13998","paper":"/paper/targeted-supervised-contrastive-learning-for","title":"Targeted Supervised Contrastive Learning for Long-Tailed Recognition","date":"2021-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lth14/targeted-supcon","path":"imagenet_inat/utils.py","file_url":"https://github.com/lth14/targeted-supcon/blob/HEAD/imagenet_inat/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"985b6559d5dbe2f7","mcp_get_code":{"code_sha256":"985b6559d5dbe2f7"}},{"arxiv_id":"2007.11978","paper":"/paper/the-devil-is-in-classification-a-simple","title":"The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation","date":"2020-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"twangnh/SimCal","path":"cls_head_models/utils.py","file_url":"https://github.com/twangnh/SimCal/blob/HEAD/cls_head_models/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":"b3ba3d62736c2db6","mcp_get_code":{"code_sha256":"b3ba3d62736c2db6"}},{"arxiv_id":"aaai_28217","paper":null,"title":"arXiv:aaai_28217","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mala-lab/COCL","path":"utils/ltr_metrics.py","file_url":"https://github.com/mala-lab/COCL/blob/HEAD/utils/ltr_metrics.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":"a7d912dd30b6673e","mcp_get_code":{"code_sha256":"a7d912dd30b6673e"}}]}