{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00198081,0.001183126,0.001022093,0.0020903,0.0004450158,0.001354587,0.002367784,0.001163878,0.001053218],"category_scores_gemma":[0.005532729,0.0004901185,0.0007049866,0.001297066,0.001424869,0.003154372,0.002359134,0.001784492,0.0004636388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112778,"about_ca_system_score_gemma":0.0008462669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003996877,"about_ca_topic_score_gemma":0.003793232,"domain_scores_codex":[0.9990838,0.0001979291,0.00003686024,0.0002526143,0.0003150168,0.0001138379],"domain_scores_gemma":[0.9973963,0.001229644,0.0004098315,0.0003238839,0.0005000873,0.0001402335],"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":[0.0003898854,0.0001789155,0.01057743,0.000181803,0.0002023582,0.0003327172,0.0002062927,0.5604109,0.01608545,0.03284562,0.004298293,0.3742903],"study_design_scores_gemma":[0.000002388384,0.00002157292,0.0002871724,0.000004285512,0.000006045311,0.00003613456,0.00001061573,0.9917952,0.002045532,0.005389114,0.0003967877,0.000005194479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03095793,0.0003399231,0.9665257,0.0002446897,0.00003396585,0.00002672813,0.00008482249,0.00107181,0.0007145298],"genre_scores_gemma":[0.6802837,0.000626177,0.3139203,0.0002713119,0.0001320493,0.00009726785,0.001042821,0.0003192755,0.003307075],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003996877,"threshold_uncertainty_score":0.01047564,"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."}}