{"id":"W3101971338","doi":"10.1109/iemcon51383.2020.9284921","title":"Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Western University","funders":"Government of Canada","keywords":"Convolutional neural network; Anomaly detection; Artificial intelligence; Computer science; Residual; Metric (unit); Anomaly (physics); Pattern recognition (psychology); Task (project management); Similarity (geometry); Artificial neural network; Deep learning; Surface (topology); Machine learning; Image (mathematics); Algorithm; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003989516,0.000853088,0.0008430532,0.001520073,0.0002397941,0.0006816507,0.001207522,0.000786228,0.001272031],"category_scores_gemma":[0.001418349,0.000386316,0.0006647162,0.0009924343,0.0004698954,0.001232417,0.001161197,0.001025519,0.0004338219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008503291,"about_ca_system_score_gemma":0.0004379214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005053139,"about_ca_topic_score_gemma":0.005645513,"domain_scores_codex":[0.999572,0.00004044872,0.00001581352,0.0001434183,0.0001568736,0.00007142221],"domain_scores_gemma":[0.9993807,0.0001345291,0.0001187176,0.0001015333,0.0002160056,0.00004861014],"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":[0.0005118369,0.0002899423,0.01120569,0.00009471228,0.000107788,0.0002824299,0.0001122392,0.443613,0.04989194,0.003940123,0.003690563,0.4862597],"study_design_scores_gemma":[0.000001819397,0.00001721774,0.0005091226,0.000001355037,0.000002676123,0.00002165258,0.000005598474,0.9965833,0.001864922,0.0008813144,0.0001084252,0.000002536188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2489522,0.0003150296,0.7450837,0.0001822347,0.00006387202,0.0000430793,0.0002260869,0.003252702,0.001881151],"genre_scores_gemma":[0.9019043,0.0001153788,0.09551387,0.00005638243,0.00003089069,0.00002600146,0.0005541256,0.0001227021,0.001676232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005053139,"threshold_uncertainty_score":0.01004744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09690244408331108,"score_gpt":0.2638881365110323,"score_spread":0.1669856924277212,"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."}}