{"id":"W2972153360","doi":"10.48550/arxiv.1909.02168","title":"Future Frame Prediction Using Convolutional VRNN for Anomaly Detection","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Autoencoder; Computer science; Anomaly detection; Benchmark (surveying); Artificial intelligence; Frame (networking); Generative grammar; Generative model; Machine learning; Anomaly (physics); Pattern recognition (psychology); Deep learning","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":[0.0007693435,0.001059163,0.0007858913,0.001340863,0.0002851406,0.0006030046,0.001506837,0.0008042027,0.001347421],"category_scores_gemma":[0.002539288,0.000386659,0.0006990037,0.0009503057,0.0003406517,0.00103886,0.000715694,0.001311243,0.0007999163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008546028,"about_ca_system_score_gemma":0.000798016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01441075,"about_ca_topic_score_gemma":0.02080753,"domain_scores_codex":[0.9995177,0.00005850798,0.00002387884,0.0002157336,0.0001021203,0.00008211697],"domain_scores_gemma":[0.9991869,0.0002563075,0.0001179967,0.0001527149,0.0002440873,0.0000419145],"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":[0.0003235211,0.0001922128,0.009001481,0.0001155504,0.0001497824,0.000291502,0.0001368844,0.2646281,0.02371833,0.005859627,0.01036428,0.6852187],"study_design_scores_gemma":[0.000002367645,0.00001464368,0.0006126959,0.000007413571,0.00001079998,0.0000360065,0.000006158622,0.99358,0.00297874,0.002162053,0.0005830572,0.000006058725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06376649,0.0009907972,0.9277057,0.0002971275,0.0001731598,0.00004663951,0.0008226257,0.004449503,0.001748014],"genre_scores_gemma":[0.8039213,0.0006587791,0.1879434,0.0001773477,0.0001342234,0.00005875059,0.002914914,0.0002875163,0.003903731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01441075,"threshold_uncertainty_score":0.02865374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05903640244747482,"score_gpt":0.1940879681459536,"score_spread":0.1350515656984787,"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."}}