{"id":"W2960634780","doi":"10.48550/arxiv.1907.06312","title":"Exploring Deep Anomaly Detection Methods Based on Capsule Net","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University; Queen's University; University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer science; Autoencoder; Pattern recognition (psychology); Benchmark (surveying); Deep learning; Classifier (UML); Anomaly detection; Anomaly (physics); Cartography","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":[0.0003525226,0.0003034486,0.0002797059,0.0004356698,0.0002248177,0.0001281776,0.001290754,0.0002448378,0.00002562524],"category_scores_gemma":[0.00002049289,0.0003628152,0.0002533701,0.0007876325,0.00004663021,0.0004066554,0.0007562083,0.0006127974,0.0001368929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003024042,"about_ca_system_score_gemma":0.00009665595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000187633,"about_ca_topic_score_gemma":0.00001978405,"domain_scores_codex":[0.9980208,0.0002056057,0.0002011329,0.001183692,0.00008936346,0.0002994107],"domain_scores_gemma":[0.997656,0.0001035195,0.0002549241,0.00173092,0.0001229158,0.000131685],"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":[0.00002941576,0.0001653852,0.000238668,0.00008058795,0.00005565564,0.00003796523,0.000102709,0.8837099,0.001714687,0.06391293,0.00005745135,0.04989464],"study_design_scores_gemma":[0.0001868403,0.0001172215,0.001023295,0.00003484802,0.00003053134,0.00000197875,0.00002204566,0.9688277,0.02121893,0.006081344,0.002057422,0.0003978598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04582474,0.00001162903,0.948518,0.00005983192,0.0005357777,0.000418173,0.000004518339,0.0007770384,0.00385023],"genre_scores_gemma":[0.9498474,0.00005668747,0.04929369,0.0001313667,0.00006852022,0.00001932533,0.000005840018,0.00002381816,0.0005533648],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9040226,"threshold_uncertainty_score":0.9998824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1768008682745736,"score_gpt":0.2324369018404793,"score_spread":0.05563603356590574,"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."}}