{"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/tripletloss","entry":"TripletLoss","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":6,"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":7,"n_samples_ran":6,"n_samples_fingerprinted":5,"n_places":7,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":6,"unverified":1},"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":"2308.04380","paper":"/paper/your-negative-may-not-be-true-negative","title":"Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luminosityx/fne","path":"loss.py","file_url":"https://github.com/luminosityx/fne/blob/HEAD/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c039a7b633159cd9","mcp_get_code":{"code_sha256":"c039a7b633159cd9"}},{"arxiv_id":"2207.06652","paper":"/paper/every-preference-changes-differently-neural","title":"Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shihui2010/mip","path":"modules/named_models.py","file_url":"https://github.com/shihui2010/mip/blob/HEAD/modules/named_models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bad0b84ee7c194b1","mcp_get_code":{"code_sha256":"bad0b84ee7c194b1"}},{"arxiv_id":"2204.02597","paper":"/paper/fine-grained-predicates-learning-for-scene","title":"Fine-Grained Predicates Learning for Scene Graph Generation","date":"2022-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinyulyu/fgpl","path":"maskrcnn_benchmark/modeling/roi_heads/relation_head/loss.py","file_url":"https://github.com/xinyulyu/fgpl/blob/HEAD/maskrcnn_benchmark/modeling/roi_heads/relation_head/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b33af276d20651f6","mcp_get_code":{"code_sha256":"b33af276d20651f6"}},{"arxiv_id":"2203.16533","paper":"/paper/large-scale-pre-training-for-person-re","title":"Large-Scale Pre-training for Person Re-identification with Noisy Labels","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ifzhang/FairMOT","path":"src/lib/models/losses.py","file_url":"https://github.com/ifzhang/FairMOT/blob/HEAD/src/lib/models/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8f7aac120b9147a0","mcp_get_code":{"code_sha256":"8f7aac120b9147a0"}},{"arxiv_id":"2006.07733","paper":"/paper/bootstrap-your-own-latent-a-new-approach-to","title":"Bootstrap your own latent: A new approach to self-supervised Learning","date":"2020-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EchoItLiu/SelfGait","path":"trainer.py","file_url":"https://github.com/EchoItLiu/SelfGait/blob/HEAD/trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e693eee1106320c1","mcp_get_code":{"code_sha256":"e693eee1106320c1"}},{"arxiv_id":"1503.03832","paper":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tamerthamoqa/facenet-pytorch-glint360k","path":"losses/triplet_loss.py","file_url":"https://github.com/tamerthamoqa/facenet-pytorch-glint360k/blob/HEAD/losses/triplet_loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"670ffaa5bf0a16fe","mcp_get_code":{"code_sha256":"670ffaa5bf0a16fe"}},{"arxiv_id":"1503.03832","paper":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iwasakishuto/DeepScreening","path":"deepscreening/tripletloss.py","file_url":"https://github.com/iwasakishuto/DeepScreening/blob/HEAD/deepscreening/tripletloss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a2a30ab2e8281ef","mcp_get_code":{"code_sha256":"3a2a30ab2e8281ef"}}]}