{"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/prototype-loss","entry":"prototype_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":5,"n_papers_ran":2,"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":5,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"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":"2303.06315","paper":"/paper/deta-denoised-task-adaptation-for-few-shot","title":"DETA: Denoised Task Adaptation for Few-Shot Learning","date":"2023-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JimZAI/DETA","path":"models/ta.py","file_url":"https://github.com/JimZAI/DETA/blob/HEAD/models/ta.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbb8ef5cff4f0915","mcp_get_code":{"code_sha256":"dbb8ef5cff4f0915"}},{"arxiv_id":"2106.14472","paper":"/paper/hyperbolic-busemann-learning-with-ideal","title":"Hyperbolic Busemann Learning with Ideal Prototypes","date":"2021-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MinaGhadimiAtigh/Hyperbolic-Busemann-Learning","path":"prototype_learning.py","file_url":"https://github.com/MinaGhadimiAtigh/Hyperbolic-Busemann-Learning/blob/HEAD/prototype_learning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4547614b2c3808b","mcp_get_code":{"code_sha256":"d4547614b2c3808b"}},{"arxiv_id":"2103.13841","paper":"/paper/universal-representation-learning-from","title":"Universal Representation Learning from Multiple Domains for Few-shot Classification","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/CoPA","path":"models/pa.py","file_url":"https://github.com/tmlr-group/CoPA/blob/HEAD/models/pa.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":"3e380e81bdaa50cd","mcp_get_code":{"code_sha256":"3e380e81bdaa50cd"}},{"arxiv_id":"2003.09338","paper":"/paper/selecting-relevant-features-from-a-universal","title":"Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification","date":"2020-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dvornikita/SUR","path":"models/losses.py","file_url":"https://github.com/dvornikita/SUR/blob/HEAD/models/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a1905f7331978663","mcp_get_code":{"code_sha256":"a1905f7331978663"}},{"arxiv_id":"2024.acl-long.502","paper":null,"title":"arXiv:2024.acl-long.502","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"GJZhang2866/HMPEAE","path":"built_prototypes.py","file_url":"https://github.com/GJZhang2866/HMPEAE/blob/HEAD/built_prototypes.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":"719536d26583680f","mcp_get_code":{"code_sha256":"719536d26583680f"}}]}