{"id":"W4391095530","doi":"10.1109/bigdata59044.2023.10386654","title":"Stabilizing Adversarial Training for Generative Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Machine learning; Generative grammar; Artificial intelligence; Classifier (UML); Adversarial system; Disjoint sets; Regularization (linguistics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005605976,0.0001588107,0.0002046567,0.00009447046,0.0003329302,0.0002051282,0.0004670241,0.00006798513,0.00002891745],"category_scores_gemma":[0.0001282449,0.0001375469,0.0001324872,0.0006230921,0.00003660182,0.0005081246,0.0001767476,0.00008156571,0.00002991211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002849991,"about_ca_system_score_gemma":0.00006770386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098777,"about_ca_topic_score_gemma":0.00002148881,"domain_scores_codex":[0.9985912,0.00008018198,0.0002198031,0.0004548491,0.0001527666,0.000501162],"domain_scores_gemma":[0.9990008,0.0004276022,0.00005945011,0.0003147864,0.00009937836,0.00009797294],"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":[0.00003341802,0.0000285266,0.00005553575,0.000008011521,0.0001102283,0.00001092378,0.003601195,0.6114698,0.003792903,0.1004772,0.03897975,0.2414325],"study_design_scores_gemma":[0.0004050221,0.00006813837,0.000110004,0.000005583181,0.000006489799,0.000001051159,0.0003034087,0.9842783,0.001568432,0.002790394,0.010268,0.0001951694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005907726,0.00003465961,0.9936482,0.001183283,0.00137058,0.0003197771,0.000003312245,0.0004326596,0.002416789],"genre_scores_gemma":[0.7291205,0.00002204786,0.2675951,0.0006331493,0.001322468,0.0001077339,0.00001707304,0.00002147963,0.001160432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7285297,"threshold_uncertainty_score":0.5609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06352876436490246,"score_gpt":0.2780805756991437,"score_spread":0.2145518113342413,"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."}}