{"id":"W4413157644","doi":"10.1109/cvpr52734.2025.01778","title":"Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Anomaly detection; Normality; Anomaly (physics); Computer science; Class (philosophy); Diffusion; Artificial intelligence; Large deviations theory; Pattern recognition (psychology); Mathematics; Statistics; Physics","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":[0.001962321,0.0009878691,0.001399198,0.001634925,0.000605359,0.001410725,0.003200964,0.002020698,0.001425919],"category_scores_gemma":[0.005309787,0.000577644,0.001419594,0.001255232,0.001464132,0.002332589,0.001504138,0.002484754,0.0007814938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568216,"about_ca_system_score_gemma":0.001655939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246515,"about_ca_topic_score_gemma":0.01209682,"domain_scores_codex":[0.9989593,0.0001966592,0.00005915635,0.000340296,0.0003276282,0.0001170006],"domain_scores_gemma":[0.9977906,0.0008462482,0.0003116542,0.0003454892,0.000593173,0.000112877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002096966,0.0001133587,0.004823573,0.0001443974,0.0001232064,0.0002261354,0.0003373658,0.6829464,0.01881457,0.04620783,0.004316115,0.2417374],"study_design_scores_gemma":[0.000004151936,0.00001237924,0.000169183,0.000003841172,0.000006064142,0.00004756222,0.000007175051,0.9923992,0.001217762,0.005519947,0.0006026536,0.00001011486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01012363,0.0001671424,0.9881909,0.0002590478,0.00003411287,0.00003817711,0.0001003111,0.0006086032,0.0004781238],"genre_scores_gemma":[0.4976659,0.0007303617,0.4917377,0.0003783455,0.0001726766,0.0002539361,0.0009823584,0.0004872195,0.00759145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01246515,"threshold_uncertainty_score":0.02478516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03518001997993168,"score_gpt":0.295088909399348,"score_spread":0.2599088894194163,"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."}}