{"id":"W4389952571","doi":"10.2139/ssrn.4668704","title":"Self-Supervised Dual-Layer 2d Normalized Flow Method for Industrial Anomaly Detection","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Download; Computer science; Anomaly detection; Dual (grammatical number); Dual layer; Anomaly (physics); Layer (electronics); Flow (mathematics); Data mining; World Wide Web; Mathematics; 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":[0.0008200333,0.0009175093,0.001127768,0.001952964,0.000444956,0.000944408,0.001464381,0.001030706,0.002173298],"category_scores_gemma":[0.001444479,0.0004917469,0.0008039009,0.001246918,0.0004166905,0.00129968,0.001130922,0.001076788,0.0009826917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003839297,"about_ca_system_score_gemma":0.001174172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003074048,"about_ca_topic_score_gemma":0.00365216,"domain_scores_codex":[0.9994791,0.00008643337,0.00002583436,0.0001426051,0.0001912164,0.00007478601],"domain_scores_gemma":[0.9994408,0.0001161218,0.0000675259,0.00009106331,0.0002478466,0.00003661445],"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":[0.0003736761,0.0003478503,0.003884328,0.0001877579,0.0001135777,0.0001020119,0.00008998512,0.1447784,0.04165757,0.004849491,0.007067589,0.7965478],"study_design_scores_gemma":[0.00000448588,0.0000204229,0.0005117313,0.000004702884,0.000007507464,0.0000308925,0.000006287983,0.9934899,0.004207138,0.0009877005,0.0007220499,0.000007213828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01460727,0.0001552027,0.982648,0.00006311754,0.00007143088,0.00003743172,0.0001506658,0.001590417,0.0006766178],"genre_scores_gemma":[0.3572793,0.0003007923,0.6354774,0.0001272131,0.0001343609,0.0001669693,0.001201648,0.0003014225,0.005010863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003074048,"threshold_uncertainty_score":0.007270455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0398110202005813,"score_gpt":0.3049174129147265,"score_spread":0.2651063927141452,"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."}}