{"id":"W2998113314","doi":"10.1109/access.2020.3041480","title":"Conditional Activation GAN: Improved Auxiliary Classifier GAN","year":2020,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Discriminator; Classifier (UML); Hyperparameter; Computation; Generative adversarial network; Normalization (sociology); Computer science; Algorithm; Pattern recognition (psychology); Artificial intelligence; Deep learning; Detector","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002700909,0.0003176412,0.000302577,0.0001744351,0.0001881479,0.001141978,0.002861341,0.0003246173,0.0000775075],"category_scores_gemma":[0.0001826904,0.0003194211,0.000123344,0.0003230825,0.00005864279,0.001218274,0.0007545237,0.001155862,0.0001464061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143225,"about_ca_system_score_gemma":0.0004040977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001697034,"about_ca_topic_score_gemma":0.00001042992,"domain_scores_codex":[0.997564,0.0001818843,0.0004166846,0.001100316,0.0004633438,0.00027378],"domain_scores_gemma":[0.9977722,0.0001576234,0.0005043769,0.001214842,0.0001835319,0.000167469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002566995,0.001227682,0.01541913,0.002449596,0.0008457899,0.0001186191,0.002902756,0.02539835,0.1398972,0.1475604,0.4122139,0.2517098],"study_design_scores_gemma":[0.0007077032,0.00007220638,0.1146923,0.0001613805,0.00004413143,0.00001093766,0.00001634435,0.7595342,0.02234584,0.0613527,0.03996074,0.00110157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003820511,0.00002824021,0.972535,0.01458917,0.001801721,0.0003972956,0.0000887656,0.0005841219,0.006155137],"genre_scores_gemma":[0.991259,0.0000195168,0.004025774,0.001925222,0.000854465,0.0001273754,0.001418282,0.00002774132,0.0003426177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9874385,"threshold_uncertainty_score":0.9999258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07755484816134543,"score_gpt":0.3434814203426344,"score_spread":0.265926572181289,"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."}}