{"id":"W3217796336","doi":"10.48550/arxiv.2111.13282","title":"Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Adversarial system; Computer science; Image translation; Feature (linguistics); Matching (statistics); Generative grammar; Image (mathematics); Artificial intelligence; Translation (biology); Interpolation (computer graphics); Algorithm; Pattern recognition (psychology); Deep learning; Theoretical computer science; Mathematics; Statistics; Linguistics; Chemistry","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":[0.001267124,0.001815477,0.001077062,0.001119751,0.0002613316,0.001445789,0.00118573,0.001608293,0.007357547],"category_scores_gemma":[0.002542975,0.0008321211,0.0009270806,0.002040607,0.001053903,0.002639156,0.001195757,0.003632136,0.003594303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009435851,"about_ca_system_score_gemma":0.0008122215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001768124,"about_ca_topic_score_gemma":0.001625471,"domain_scores_codex":[0.9994414,0.0001807624,0.0000388782,0.0001350003,0.0001668996,0.00003699202],"domain_scores_gemma":[0.9988623,0.000884744,0.00003559563,0.00009534472,0.00009541194,0.00002670926],"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":[0.00004984872,0.0001412316,0.001040915,0.001523339,0.0001552402,0.0001750908,0.0001617627,0.1530622,0.00185788,0.2843935,0.04931171,0.5081272],"study_design_scores_gemma":[0.00001937813,0.0001035383,0.0008881938,0.0007398733,0.00007013351,0.000638008,0.00005760668,0.35051,0.002072965,0.3058722,0.3389491,0.00007886466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002077037,0.1152602,0.8371241,0.002215196,0.0009426972,0.00009626962,0.0004828391,0.0009902747,0.04081143],"genre_scores_gemma":[0.1310088,0.4304063,0.3786366,0.003306859,0.004723023,0.0007294234,0.00309767,0.001233988,0.04685744],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007357547,"threshold_uncertainty_score":0.02461344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0570249352263426,"score_gpt":0.1813410513822528,"score_spread":0.1243161161559102,"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."}}