{"id":"W3093318798","doi":"10.48550/arxiv.2010.08534","title":"Latent Vector Recovery of Audio GANs","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Residual; Artificial neural network; Speech recognition; Encoder; Pattern recognition (psychology); Algorithm","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":[0.0008048736,0.0008847011,0.0004628642,0.0003915723,0.0001435681,0.0004457673,0.0007215088,0.0005466903,0.002320951],"category_scores_gemma":[0.002724679,0.0003265796,0.0005264686,0.0003212818,0.0006151503,0.000822451,0.000945324,0.001440737,0.0006535318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004695634,"about_ca_system_score_gemma":0.0003650246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500293,"about_ca_topic_score_gemma":0.002052475,"domain_scores_codex":[0.9996816,0.0001073834,0.00001066463,0.00007828068,0.00008088366,0.00004125117],"domain_scores_gemma":[0.9993055,0.0004041054,0.0000636281,0.0001063258,0.00009182357,0.00002857896],"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":[0.0001647362,0.00003551625,0.0005429096,0.00008060532,0.00004320215,0.0001052935,0.00005564126,0.8822103,0.01106093,0.01967573,0.002493419,0.08353168],"study_design_scores_gemma":[0.000004635333,0.00001062817,0.000077377,0.000005299728,0.00000300906,0.00001669187,0.000004113137,0.9932014,0.001735247,0.004504385,0.000433713,0.00000346171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01539003,0.0002008036,0.9816355,0.0001408914,0.00003799447,0.00002165504,0.0001359436,0.0007087277,0.001728575],"genre_scores_gemma":[0.7833032,0.0004937894,0.2049558,0.0002506548,0.00009294434,0.0001164163,0.0009672719,0.0003348386,0.009485175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002320951,"threshold_uncertainty_score":0.007764339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09340267472210834,"score_gpt":0.179689433303894,"score_spread":0.08628675858178564,"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."}}