{"id":"W2979332623","doi":"10.48550/arxiv.1910.04241","title":"Out-of-distribution Detection in Classifiers via Generation","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"MNIST database; Autoencoder; Classifier (UML); Artificial intelligence; Computer science; Inference; Artificial neural network; Machine learning; Pattern recognition (psychology); Detector","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":[0.0003822134,0.0002193306,0.0002846234,0.0002602081,0.00007522448,0.00004757519,0.0008890816,0.0003769735,0.000008558583],"category_scores_gemma":[0.00007732002,0.0002807966,0.0001320032,0.0005143704,0.00005825132,0.0004391142,0.0009340977,0.0007493888,0.00003788064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005437526,"about_ca_system_score_gemma":0.0001472318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002116894,"about_ca_topic_score_gemma":0.0001683312,"domain_scores_codex":[0.998328,0.0002635698,0.0002507575,0.0008109913,0.0001086545,0.0002379846],"domain_scores_gemma":[0.9985634,0.00006681086,0.0003925484,0.0007925948,0.0001278697,0.00005677815],"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":[0.00001748611,0.00003191626,0.002418462,0.0000436683,0.00002001741,0.00002539458,0.0001570715,0.9809306,0.0007558521,0.01259745,0.00002183117,0.00298029],"study_design_scores_gemma":[0.0003689887,0.00003790902,0.00209999,0.00004509163,0.00002109143,8.965005e-7,0.00002589842,0.9911521,0.001079723,0.004811046,0.0001036975,0.0002535388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1729223,0.00001303868,0.8244031,0.00003312455,0.001992709,0.000222569,0.00000491149,0.00009159499,0.0003166717],"genre_scores_gemma":[0.9982335,0.00002677759,0.001353649,0.00001333824,0.000101186,7.419768e-7,0.00005811112,0.00001157949,0.0002011145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8253112,"threshold_uncertainty_score":0.9999644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07386632659431568,"score_gpt":0.2077521175497941,"score_spread":0.1338857909554785,"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."}}