{"id":"W3205111034","doi":"10.1109/icas49788.2021.9551183","title":"Attentive Autoencoders For Improving Visual Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Computer science; Anomaly detection; Artificial intelligence; Hyperparameter; Modular design; Machine learning; Visualization; Deep learning; Pattern recognition (psychology)","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":[0.00008662415,0.0000711363,0.0000703136,0.00004901618,0.0002137378,0.0001206611,0.0001718528,0.00004630282,0.00001787776],"category_scores_gemma":[0.00001922826,0.00007123838,0.00008340574,0.0003096437,0.00001509385,0.0002741115,0.00009832413,0.00005186287,0.0000158601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004041348,"about_ca_system_score_gemma":0.00004540744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002760311,"about_ca_topic_score_gemma":0.00004879929,"domain_scores_codex":[0.9993045,0.00001347164,0.0001306729,0.0003208045,0.00007824184,0.0001523286],"domain_scores_gemma":[0.9994836,0.00003549654,0.00005123481,0.0002273621,0.0001579742,0.00004430488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005872395,0.0001298415,0.0001721518,0.00002684783,0.00002298168,0.00000369686,0.000142363,0.00004985746,0.2338002,0.06824733,0.0003026012,0.6970963],"study_design_scores_gemma":[0.0001536702,0.0001076413,0.001019937,0.000001859198,0.000006706805,0.00002165434,0.0000882509,0.4059511,0.583995,0.00298548,0.005519381,0.0001492325],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007819841,0.00001421247,0.9894328,0.0004653164,0.000111091,0.0001815662,0.000001096423,0.0005107336,0.001463359],"genre_scores_gemma":[0.7835909,0.000001425056,0.214495,0.0002511942,0.00004094403,0.0001438993,0.000001592762,0.000005663803,0.001469306],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7757711,"threshold_uncertainty_score":0.2905017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044808267533026,"score_gpt":0.2627904296225756,"score_spread":0.2523423469472454,"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."}}