{"id":"W4382469210","doi":"10.1609/aaai.v37i9.26320","title":"Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generalization; Domain (mathematical analysis); Artificial intelligence; Machine learning; Representation (politics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009931579,0.0001595413,0.0001637267,0.0002323655,0.0001957656,0.0003718014,0.001603642,0.0000657283,0.00003708991],"category_scores_gemma":[0.0002206155,0.000139833,0.00005103756,0.000737578,0.0001106963,0.002055289,0.0004550734,0.0001428801,0.00008180364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006837337,"about_ca_system_score_gemma":0.00006002242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000166659,"about_ca_topic_score_gemma":0.00001183358,"domain_scores_codex":[0.9981363,0.00002607091,0.0005016685,0.0005565445,0.0004853117,0.0002940748],"domain_scores_gemma":[0.9989862,0.0001230019,0.0003116982,0.0003561009,0.0001670255,0.00005593045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009617379,0.0001231618,0.0004144979,0.00006347595,0.00001468384,7.621413e-7,0.00611759,0.01174481,0.03017006,0.7305086,0.0006133224,0.2201329],"study_design_scores_gemma":[0.0001012072,0.0001181602,0.001039146,0.0001450128,0.000005247774,9.697774e-7,0.003947375,0.8145157,0.02139156,0.1573333,0.001213031,0.0001893113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069507,0.00003779516,0.8765483,0.01299222,0.0007227783,0.001486285,0.00002096591,0.0001400623,0.001100926],"genre_scores_gemma":[0.9797114,0.0003058645,0.01888927,0.0003000494,0.00006356739,0.0001136255,0.00005969631,0.00001820882,0.0005382993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8727608,"threshold_uncertainty_score":0.5702223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1258014299904242,"score_gpt":0.3263520083042971,"score_spread":0.2005505783138729,"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."}}