{"id":"W4390413835","doi":"10.1016/j.imavis.2023.104897","title":"Exploiting classifier inter-level features for efficient out-of-distribution detection","year":2023,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs","keywords":"Computer science; Classifier (UML); Inference; Artificial intelligence; Exploit; Deep learning; Software deployment; Training set; Machine learning; Network architecture; Architecture; Pattern recognition (psychology); Data mining","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":[0.0007740692,0.0007835321,0.001141469,0.001664545,0.0006263768,0.001321202,0.001037785,0.0009045873,0.001316815],"category_scores_gemma":[0.002763469,0.0003083828,0.0005187782,0.001331463,0.0002764972,0.001371168,0.001190261,0.001417838,0.001191493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000570006,"about_ca_system_score_gemma":0.0008790654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003043698,"about_ca_topic_score_gemma":0.005738832,"domain_scores_codex":[0.9992533,0.00007059534,0.00003439719,0.0001393183,0.0003360158,0.00016621],"domain_scores_gemma":[0.9982833,0.0005428029,0.0002299593,0.0003230633,0.0005352429,0.00008572989],"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":[0.0005363675,0.000576042,0.02251703,0.00008071856,0.0001100587,0.0004041552,0.0001173603,0.03343821,0.1271538,0.004616441,0.006494467,0.8039554],"study_design_scores_gemma":[0.00001087475,0.00007989437,0.006161889,0.00000636045,0.00002682115,0.0002745561,0.0000390407,0.9562644,0.03012012,0.004642246,0.002355631,0.00001817746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08973818,0.0002294198,0.9049408,0.0002074239,0.00009645048,0.00005006563,0.0002049157,0.002959875,0.001572752],"genre_scores_gemma":[0.8014949,0.0001611826,0.194733,0.0001241982,0.00009283741,0.00004960815,0.0007012028,0.0002938565,0.00234919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003043698,"threshold_uncertainty_score":0.006052017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03884751640672859,"score_gpt":0.3427141217430519,"score_spread":0.3038666053363234,"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."}}