{"id":"W4415231940","doi":"10.2139/ssrn.5611717","title":"SODA: Stabilized Optimal Transport Based Domain Alignment for Domain Generalization via a Dynamic Feature Reservoir","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Generalization; Domain (mathematical analysis); Feature (linguistics); Stability (learning theory); Convergence (economics); Variance (accounting); Instability; Work (physics)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006586171,0.0007541907,0.001482776,0.000569894,0.0006347655,0.001009073,0.002059798,0.001849588,0.003759223],"category_scores_gemma":[0.001990451,0.0005213572,0.0007835664,0.0007022892,0.001032046,0.002080582,0.003413553,0.002227853,0.001355774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006579054,"about_ca_system_score_gemma":0.001254649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003214575,"about_ca_topic_score_gemma":0.003815583,"domain_scores_codex":[0.9997119,0.00005602048,0.00001499308,0.0001081814,0.00006764536,0.000041141],"domain_scores_gemma":[0.9994603,0.0002052583,0.00003985056,0.0001540326,0.00007630463,0.00006434306],"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":[0.000521726,0.0002277154,0.0008703273,0.0002093027,0.000136193,0.0002513384,0.0001721898,0.5092623,0.04702271,0.05992732,0.01151917,0.3698796],"study_design_scores_gemma":[0.000006335968,0.00001689703,0.00003186841,0.000002568411,0.000003127645,0.00001429371,0.000005510483,0.9901327,0.002022696,0.007311838,0.0004471868,0.000005024688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01167189,0.0001144736,0.9853951,0.0001357195,0.000073889,0.00003541908,0.0001022956,0.001680078,0.0007910961],"genre_scores_gemma":[0.4237949,0.0001796467,0.5671876,0.000303058,0.00008565444,0.0002014287,0.0007430057,0.0006240953,0.006880671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003759223,"threshold_uncertainty_score":0.01257586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009430487744405984,"score_gpt":0.262997887744679,"score_spread":0.253567400000273,"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."}}