{"id":"W3133289359","doi":"10.1016/j.neucom.2020.09.091","title":"Domain generalization via optimal transport with metric similarity learning","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; Western University; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Leverage (statistics); Artificial intelligence; Computer science; Similarity (geometry); Generalization; Invariant (physics); Machine learning; Domain (mathematical analysis); Pattern recognition (psychology); Metric (unit); Boundary (topology); Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001289396,0.000627169,0.001606791,0.001002865,0.0005738561,0.0008906955,0.001633314,0.001857683,0.001617172],"category_scores_gemma":[0.00405428,0.0005118118,0.001258783,0.0009476822,0.001224925,0.002589171,0.002846321,0.001915153,0.000420921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009769989,"about_ca_system_score_gemma":0.0009756841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00488088,"about_ca_topic_score_gemma":0.003437467,"domain_scores_codex":[0.9995499,0.0001571854,0.00002696498,0.0001421723,0.00008456929,0.00003911629],"domain_scores_gemma":[0.9988593,0.0005524076,0.00009163931,0.0002458708,0.0001745332,0.00007619063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001672665,0.0001557321,0.0009956773,0.0001880617,0.0001537465,0.0001100695,0.0001442054,0.7159479,0.009275679,0.0851738,0.004485469,0.1832023],"study_design_scores_gemma":[0.000003535351,0.00001512364,0.00005984356,0.000003739741,0.000004491757,0.00001679982,0.000006231205,0.9775285,0.0005265183,0.02155213,0.0002771818,0.000005887659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01276893,0.0002681907,0.9857119,0.0001838649,0.00004244651,0.00002350053,0.00003943442,0.0002655831,0.0006961539],"genre_scores_gemma":[0.6116963,0.0006966101,0.3790359,0.000331707,0.0001483374,0.000196049,0.0004662345,0.0003045276,0.007124327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00488088,"threshold_uncertainty_score":0.009704947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166318286433433,"score_gpt":0.2247388399529125,"score_spread":0.2130756570885782,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}