{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612053,0.001106626,0.001125842,0.0007650524,0.0004484214,0.0007917137,0.002111049,0.001427114,0.001374833],"category_scores_gemma":[0.004237863,0.0005509064,0.001350948,0.0008632758,0.001183834,0.003194279,0.001947967,0.002739385,0.000563403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006430629,"about_ca_system_score_gemma":0.0008695982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0029558,"about_ca_topic_score_gemma":0.003380469,"domain_scores_codex":[0.9993207,0.0002158794,0.0000317282,0.000277192,0.00009168681,0.00006279142],"domain_scores_gemma":[0.9983671,0.0006994865,0.0001197636,0.0005625883,0.0001511815,0.00009991461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003229027,0.0003643413,0.003312239,0.0001826902,0.000133371,0.0002394931,0.0003415734,0.6442824,0.01208313,0.01189402,0.006783757,0.3200601],"study_design_scores_gemma":[0.00001054917,0.0000438862,0.0001930525,0.00000918059,0.00001025275,0.00005041184,0.00002744506,0.9858947,0.002048231,0.01062404,0.001078,0.00001027427],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03608692,0.0006081133,0.9597622,0.0004801566,0.00006758309,0.00007955902,0.0002408014,0.001597863,0.001076862],"genre_scores_gemma":[0.5970125,0.0004785021,0.3972412,0.0007215915,0.0001236911,0.0002398169,0.001450824,0.0002341811,0.002497661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0029558,"threshold_uncertainty_score":0.008525431,"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."}}