{"id":"W4400381602","doi":"10.1016/j.asoc.2024.111928","title":"Self-supervised dual-layer 2D normalizing flow method for industrial anomaly detection","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Anomaly detection; Dual (grammatical number); Computer science; Anomaly (physics); Flow (mathematics); Dual layer; Layer (electronics); Pattern recognition (psychology); Artificial intelligence; Mathematics; Materials science; Physics; Composite material; Geometry","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":[0.0007804154,0.0009543911,0.001031368,0.002078843,0.0005472599,0.0009505887,0.001563227,0.0008885349,0.002669961],"category_scores_gemma":[0.001179672,0.0004774241,0.0009179474,0.001358439,0.0004031588,0.001289939,0.001076775,0.001066605,0.00105486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004873656,"about_ca_system_score_gemma":0.00145775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004849568,"about_ca_topic_score_gemma":0.006619483,"domain_scores_codex":[0.9994782,0.00006153097,0.00002554398,0.0001415333,0.0002126641,0.0000805736],"domain_scores_gemma":[0.9995294,0.00007866327,0.00005338759,0.00007498181,0.0002335751,0.00003008966],"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":[0.0002719022,0.0003226687,0.003584099,0.0001311666,0.00009338542,0.00008521444,0.00008767036,0.09352394,0.04000062,0.004770617,0.006982648,0.8501462],"study_design_scores_gemma":[0.000005572007,0.00002312709,0.0007186996,0.000005393466,0.00001079775,0.00003714421,0.000009843664,0.98981,0.007032427,0.001124211,0.001213039,0.000009874767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01399328,0.0001400878,0.9825943,0.00006732519,0.00007182247,0.00004528694,0.0001396368,0.002106772,0.0008415505],"genre_scores_gemma":[0.3094797,0.0002992664,0.6818818,0.0001411867,0.0001072771,0.0001759687,0.001147037,0.0003640298,0.00640371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004849568,"threshold_uncertainty_score":0.00964272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082451740863196,"score_gpt":0.2875966023330008,"score_spread":0.2567720849243688,"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."}}