{"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/random-pairs-of-minibatches","entry":"random_pairs_of_minibatches","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":8,"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":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"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":"2605.16372","paper":"/paper/arxiv-2605-16372","title":"SWORDBENCH: Evaluating Orthogonality of Steering Image Representations","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Weixin-Liang/MetaShift","path":"experiments/distribution_shift/algorithms.py","file_url":"https://github.com/Weixin-Liang/MetaShift/blob/HEAD/experiments/distribution_shift/algorithms.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2407.15085","paper":"/paper/learn-to-preserve-and-diversify-parameter","title":"Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization","date":"2024-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JudgingH/PEGO","path":"domainbed/lib/misc.py","file_url":"https://github.com/JudgingH/PEGO/blob/HEAD/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2310.08255","paper":"/paper/distilling-from-vision-language-models-for","title":"Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"val-iisc/VL2V-ADiP","path":"domainbed/lib/misc.py","file_url":"https://github.com/val-iisc/VL2V-ADiP/blob/HEAD/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2305.13046","paper":"/paper/poem-polarization-of-embeddings-for-domain","title":"POEM: Polarization of Embeddings for Domain-Invariant Representations","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoSangYoung/Official-POEM","path":"POEM/domainbed/lib/misc.py","file_url":"https://github.com/JoSangYoung/Official-POEM/blob/HEAD/POEM/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2303.01233","paper":"/paper/domain-aware-triplet-loss-in-domain","title":"Domain-aware Triplet loss in Domain Generalization","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"workerbcd/dct","path":"domainbed/lib/misc.py","file_url":"https://github.com/workerbcd/dct/blob/HEAD/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2302.14685","paper":"/paper/dart-diversify-aggregate-repeat-training","title":"DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks","date":"2023-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"val-iisc/DART","path":"domainbed/lib/misc.py","file_url":"https://github.com/val-iisc/DART/blob/HEAD/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2203.10789","paper":"/paper/domain-generalization-by-mutual-information","title":"Domain Generalization by Mutual-Information Regularization with Pre-trained Models","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/miro","path":"domainbed/lib/misc.py","file_url":"https://github.com/kakaobrain/miro/blob/HEAD/domainbed/lib/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"2110.09410","paper":"/paper/exploiting-domain-specific-features-to","title":"Exploiting Domain-Specific Features to Enhance Domain Generalization","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinairesearch/mdsdi","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/vinairesearch/mdsdi/blob/HEAD/DomainBed/domainbed/algorithms.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4fa2b54178fcc1df","mcp_get_code":{"code_sha256":"4fa2b54178fcc1df"}},{"arxiv_id":"aaai_28994","paper":null,"title":"arXiv:aaai_28994","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Alrash/EDM","path":"src/DeepDG/datautil/util.py","file_url":"https://github.com/Alrash/EDM/blob/HEAD/src/DeepDG/datautil/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fff95ee8729333e4","mcp_get_code":{"code_sha256":"fff95ee8729333e4"}}]}