{"id":"W4318977982","doi":"10.1007/978-3-031-10602-6_21","title":"Adversarial Autoencoders","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adversarial system; Generative grammar; Computer science; Point (geometry); Artificial intelligence; Noise (video); Sample (material); Quality (philosophy); Pattern recognition (psychology); Machine learning; Mathematics; Image (mathematics); Epistemology; Philosophy","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":[0.0003910103,0.0009647254,0.0005369695,0.0004831973,0.0002316084,0.001069828,0.00069398,0.000891111,0.0158703],"category_scores_gemma":[0.001623049,0.0003591203,0.0003902242,0.0005588165,0.0007966555,0.001491919,0.001098943,0.001961369,0.0102213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003862359,"about_ca_system_score_gemma":0.0002701068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004871023,"about_ca_topic_score_gemma":0.0007276349,"domain_scores_codex":[0.9997165,0.00004946491,0.0000102808,0.00005270561,0.0001557435,0.0000153317],"domain_scores_gemma":[0.9995849,0.0002120881,0.00001776828,0.0001053617,0.00007073455,0.000009210216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002540389,0.00003884292,0.0001175714,0.0001916332,0.00004729386,0.00007595507,0.00004576793,0.1167038,0.004525992,0.2917922,0.08300213,0.5034334],"study_design_scores_gemma":[0.000005813904,0.00003749461,0.0003211315,0.000156484,0.00003014637,0.0003102947,0.00001883373,0.374073,0.009151377,0.3303684,0.2854841,0.00004295709],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001321887,0.004946997,0.8555296,0.0007244513,0.000779035,0.00003262539,0.0002094907,0.001249166,0.1352067],"genre_scores_gemma":[0.1359239,0.01659523,0.2685105,0.001459231,0.001727225,0.0001897547,0.00147795,0.001640462,0.5724759],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0158703,"threshold_uncertainty_score":0.05309147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503644518092707,"score_gpt":0.2299525585263899,"score_spread":0.2149161133454628,"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."}}