{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008575504,0.0009381111,0.0005856591,0.0006803017,0.0002460463,0.000617298,0.001040887,0.0007541861,0.001301227],"category_scores_gemma":[0.003817135,0.0003390208,0.000582237,0.0004656376,0.0006619062,0.001329613,0.00127296,0.001702846,0.0005423129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004781573,"about_ca_system_score_gemma":0.0004594073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003243453,"about_ca_topic_score_gemma":0.004061976,"domain_scores_codex":[0.9995814,0.00009097056,0.00002139897,0.000140293,0.0001096845,0.00005629313],"domain_scores_gemma":[0.9986495,0.0007434057,0.0001212737,0.0001781732,0.0002627021,0.00004482391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001645385,0.0001896321,0.002261794,0.0001457654,0.0001232786,0.0001633632,0.0001914147,0.3284514,0.054379,0.007718707,0.004372211,0.6018389],"study_design_scores_gemma":[0.000002755993,0.00002152861,0.0005454804,0.000008649458,0.00001269252,0.00004156339,0.00001097244,0.9883124,0.006998032,0.003472538,0.0005671674,0.000006235553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04968293,0.0006874377,0.9454601,0.0002704904,0.00009559535,0.00003068054,0.00007892701,0.002077928,0.001615932],"genre_scores_gemma":[0.731324,0.0006760837,0.2628154,0.0003962798,0.000158967,0.00005369116,0.0004063546,0.0002623167,0.003907106],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003243453,"threshold_uncertainty_score":0.006449163,"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."}}