{"id":"W4404884731","doi":"10.1007/978-3-031-78128-5_13","title":"Label-Expanded Feature Debiasing for Single Domain Generalization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Debiasing; Computer science; Generalization; Feature (linguistics); Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Mathematics; Cognitive science","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":[0.00151167,0.0008873275,0.001627618,0.0008509719,0.0006961381,0.0006847017,0.002299583,0.001624703,0.006565879],"category_scores_gemma":[0.003286867,0.0004228933,0.001140106,0.000983424,0.0008814659,0.002284181,0.002755718,0.002984273,0.002274062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006025433,"about_ca_system_score_gemma":0.0007709897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004034144,"about_ca_topic_score_gemma":0.005869905,"domain_scores_codex":[0.999303,0.0001317384,0.00003970118,0.0002801534,0.0001517402,0.00009370264],"domain_scores_gemma":[0.9985012,0.0004898077,0.00005262343,0.0006809799,0.0002142659,0.00006121173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003200295,0.0001574711,0.0004460162,0.0001456279,0.0000829789,0.0001189589,0.0001098968,0.06022219,0.02171399,0.01278355,0.01540751,0.8884917],"study_design_scores_gemma":[0.0000204936,0.00006432614,0.0003867394,0.0000208787,0.0000317052,0.0001138296,0.00003858993,0.9559174,0.01098349,0.02856233,0.00383986,0.00002035432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01232605,0.0006018895,0.9813895,0.0001861259,0.0001202903,0.00005742245,0.0001978845,0.003430797,0.001690093],"genre_scores_gemma":[0.3434995,0.0006149193,0.6382054,0.000608609,0.0001952234,0.0001824553,0.002124983,0.0009901912,0.01357871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006565879,"threshold_uncertainty_score":0.02196503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289085202236165,"score_gpt":0.2673624240164627,"score_spread":0.2384539037928461,"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."}}