{"id":"W3088878551","doi":"10.1109/access.2020.3025530","title":"Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Generative grammar; Data mining; Class (philosophy)","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":[0.001895998,0.00100563,0.0006596159,0.0005221947,0.0002861608,0.0007008433,0.002267761,0.000974287,0.002504716],"category_scores_gemma":[0.00373522,0.0005081464,0.0006802392,0.0003423168,0.0008930529,0.001082983,0.001363099,0.002897978,0.0008621307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151064,"about_ca_system_score_gemma":0.001153843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002825954,"about_ca_topic_score_gemma":0.003241425,"domain_scores_codex":[0.9992365,0.000238018,0.00003133334,0.0001989162,0.0002265688,0.00006875836],"domain_scores_gemma":[0.9988187,0.0005824326,0.00009507671,0.0001358414,0.0002901124,0.00007769331],"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":[0.00007858335,0.00009883688,0.001033488,0.00007509626,0.00006676117,0.00008806577,0.00003651699,0.8908518,0.004123403,0.01412973,0.003262031,0.08615568],"study_design_scores_gemma":[0.000003463637,0.00001614166,0.00003875415,0.000002978263,0.000002505859,0.00001304263,0.000001487633,0.996996,0.000818598,0.001677433,0.0004269107,0.0000027349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006536118,0.0001893138,0.9889936,0.000244557,0.00005354323,0.0001620344,0.000102401,0.001718887,0.001999498],"genre_scores_gemma":[0.4191298,0.0003625982,0.57322,0.0007298578,0.00007410012,0.0007648888,0.0007243957,0.0003389629,0.004655492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002825954,"threshold_uncertainty_score":0.01002711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06074284376028855,"score_gpt":0.2960437699990981,"score_spread":0.2353009262388095,"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."}}