{"id":"W2990943125","doi":"10.48550/arxiv.1911.10608","title":"AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Computation; Enhanced Data Rates for GSM Evolution; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002633194,0.0003581445,0.0004650403,0.0005140385,0.00006083746,0.00007298355,0.0002899723,0.0008528588,0.00007847239],"category_scores_gemma":[0.00001875201,0.0004267467,0.0001810132,0.0006173402,0.00002604923,0.0002044595,0.0001583204,0.0008636256,0.0002341038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000422306,"about_ca_system_score_gemma":0.00005648717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008023171,"about_ca_topic_score_gemma":0.0007868595,"domain_scores_codex":[0.9985261,0.0001274586,0.0003005988,0.000619564,0.0000898522,0.0003364426],"domain_scores_gemma":[0.999103,0.00006091377,0.0001009368,0.0005771014,0.00007029495,0.00008776285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008407755,0.00002159896,0.00390177,0.0001553672,0.00006622435,0.0000631741,0.0001014482,0.9890901,0.005485794,0.00006599632,0.0001441329,0.0008203071],"study_design_scores_gemma":[0.001579488,0.0001129146,0.0144131,0.0002838677,0.00007488937,0.000007807245,0.0003661302,0.9708767,0.008967592,0.0004248562,0.001951918,0.0009407708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832469,0.0001607499,0.007899228,0.000003203478,0.002547717,0.0005873864,0.00002541752,0.0004028293,0.005126631],"genre_scores_gemma":[0.9987212,0.0001209528,0.00001368757,0.000006089012,0.0001636879,0.000001870548,0.00001723855,0.00005363146,0.0009016627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01821344,"threshold_uncertainty_score":0.9998184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0476470463432403,"score_gpt":0.1670380667585388,"score_spread":0.1193910204152985,"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."}}