{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009862512,0.001037828,0.0009878781,0.0004951337,0.0002026787,0.000727786,0.001572825,0.0009227603,0.004574576],"category_scores_gemma":[0.002704959,0.0003601942,0.0007015153,0.0005922119,0.0006538732,0.001161295,0.0009722761,0.002164577,0.001822106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006346445,"about_ca_system_score_gemma":0.00087156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878428,"about_ca_topic_score_gemma":0.003246789,"domain_scores_codex":[0.9993481,0.0002092442,0.00002045122,0.0001540906,0.0001819957,0.00008604342],"domain_scores_gemma":[0.999105,0.0004264622,0.00004880648,0.0001775048,0.0001972845,0.00004495191],"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":[0.0002304197,0.0001318209,0.001386969,0.0001264213,0.00009308697,0.0001812864,0.0000835836,0.7194043,0.01202406,0.07690671,0.02856112,0.1608702],"study_design_scores_gemma":[0.000007212085,0.00001453161,0.00008901113,0.000005494393,0.000006616041,0.00002455489,0.000001924493,0.9884521,0.0008284862,0.008790719,0.001775277,0.00000408119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008212232,0.0002834678,0.9832826,0.0002400427,0.00008244154,0.00004950043,0.0003362157,0.001614879,0.005898733],"genre_scores_gemma":[0.6436909,0.0007096232,0.3267659,0.001112675,0.000272974,0.0003834718,0.003823918,0.001027409,0.02221308],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004574576,"threshold_uncertainty_score":0.01530343,"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."}}