{"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/gradreverse","entry":"GradReverse","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":12,"n_papers_ran":12,"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":13,"n_samples_ran":13,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":13,"unverified":0},"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":"2607.19400","paper":"/paper/arxiv-2607-19400","title":"TA B U L A Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"aristoteleo/tabula","path":"tabula/model/transfomer/transformer.py","file_url":"https://github.com/aristoteleo/tabula/blob/HEAD/tabula/model/transfomer/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f94ab6c27f44a442","mcp_get_code":{"code_sha256":"f94ab6c27f44a442"}},{"arxiv_id":"2601.03090","paper":"/paper/arxiv-2601-03090","title":"Lesiontabe: Equitable AI for Skin Lesion Detection","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"rociomexiadiaz/DermieAI","path":"TABE.py","file_url":"https://github.com/rociomexiadiaz/DermieAI/blob/HEAD/TABE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8546d744120c95b3","mcp_get_code":{"code_sha256":"8546d744120c95b3"}},{"arxiv_id":"2208.07365","paper":"/paper/unsupervised-video-domain-adaptation-for-1","title":"Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective","date":"2022-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ldkong1205/transvae","path":"exp/TranSVAE.py","file_url":"https://github.com/ldkong1205/transvae/blob/HEAD/exp/TranSVAE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ef67ff5a31f0abd","mcp_get_code":{"code_sha256":"1ef67ff5a31f0abd"}},{"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","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ebff29d9d1f6869c","mcp_get_code":{"code_sha256":"ebff29d9d1f6869c"}},{"arxiv_id":"2109.02038","paper":"/paper/nas-ood-neural-architecture-search-for-out-of","title":"NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization","date":"2021-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaoyueBaiZJU/NAS-OoD","path":"nas_ood_single/search/models/model_search.py","file_url":"https://github.com/HaoyueBaiZJU/NAS-OoD/blob/HEAD/nas_ood_single/search/models/model_search.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"033c37e041ba38d1","mcp_get_code":{"code_sha256":"033c37e041ba38d1"}},{"arxiv_id":"2106.04732","paper":"/paper/adamatch-a-unified-approach-to-semi","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zwenyu/UniSSDA","path":"algorithms/algorithms.py","file_url":"https://github.com/zwenyu/UniSSDA/blob/HEAD/algorithms/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b47a2ddfc97b026","mcp_get_code":{"code_sha256":"2b47a2ddfc97b026"}},{"arxiv_id":"2104.00246","paper":"/paper/divergence-optimization-for-noisy-universal","title":"Divergence Optimization for Noisy Universal Domain Adaptation","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yu1ut/divergence-optimization","path":"models/basenet.py","file_url":"https://github.com/yu1ut/divergence-optimization/blob/HEAD/models/basenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1106e6e01a4ecdd0","mcp_get_code":{"code_sha256":"1106e6e01a4ecdd0"}},{"arxiv_id":"2009.13504","paper":"/paper/graph-adversarial-networks-protecting","title":"Information Obfuscation of Graph Neural Networks","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liaopeiyuan/GAL","path":"Movielens_Wasserstein/models.py","file_url":"https://github.com/liaopeiyuan/GAL/blob/HEAD/Movielens_Wasserstein/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"764b8adf807dbb5c","mcp_get_code":{"code_sha256":"764b8adf807dbb5c"}},{"arxiv_id":"2003.00845","paper":"/paper/addressing-target-shift-in-zero-shot-learning","title":"Addressing target shift in zero-shot learning using grouped adversarial learning","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mvp18/gAL-MELEX","path":"APY/code/model.py","file_url":"https://github.com/mvp18/gAL-MELEX/blob/HEAD/APY/code/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a9a5eb577f20e70","mcp_get_code":{"code_sha256":"5a9a5eb577f20e70"}},{"arxiv_id":"2002.07366","paper":"/paper/adversarial-deep-network-embedding-for-cross","title":"Adversarial Deep Network Embedding for Cross-network Node Classification","date":"2020-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"3480430977/ACDNE","path":"ACDNE_model.py","file_url":"https://github.com/3480430977/ACDNE/blob/HEAD/ACDNE_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80d8c5bd7811d991","mcp_get_code":{"code_sha256":"80d8c5bd7811d991"}},{"arxiv_id":"1505.07818","paper":"/paper/domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","date":"2015-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzx6/pytorch_DA","path":"models/model.py","file_url":"https://github.com/lzx6/pytorch_DA/blob/HEAD/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"22d411c2795ced15","mcp_get_code":{"code_sha256":"22d411c2795ced15"}},{"arxiv_id":"1505.07818","paper":"/paper/domain-adversarial-training-of-neural","title":"Domain-Adversarial Training of Neural Networks","date":"2015-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"calico/scnym","path":"scnym/model.py","file_url":"https://github.com/calico/scnym/blob/HEAD/scnym/model.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":"1a6a7062274a346d","mcp_get_code":{"code_sha256":"1a6a7062274a346d"}},{"arxiv_id":"1409.7495","paper":"/paper/unsupervised-domain-adaptation-by","title":"Unsupervised Domain Adaptation by Backpropagation","date":"2014-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ermolenkodev/da-ssd","path":"da_ssd/model/da.py","file_url":"https://github.com/ermolenkodev/da-ssd/blob/HEAD/da_ssd/model/da.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"20cb3aaf5c97f956","mcp_get_code":{"code_sha256":"20cb3aaf5c97f956"}}]}