{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008466647,0.0006813259,0.0008352938,0.0002019173,0.0004863426,0.0008617141,0.001054466,0.0006931412,0.00003742276],"category_scores_gemma":[0.0002331983,0.0007736654,0.000226017,0.0005908048,0.0003756329,0.001022515,0.003833913,0.0009214209,0.000004664785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001834357,"about_ca_system_score_gemma":0.0004891924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001613007,"about_ca_topic_score_gemma":0.0009932485,"domain_scores_codex":[0.9953054,0.001231508,0.0003845043,0.002252049,0.000182755,0.0006437359],"domain_scores_gemma":[0.9972292,0.0005241404,0.0003692747,0.001081477,0.0003977616,0.0003981973],"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":[0.0002082509,0.00009180154,0.002367315,0.00003132236,0.0004608693,0.0003855566,0.000693802,0.9843524,0.0000407332,0.00879251,0.001166375,0.001409085],"study_design_scores_gemma":[0.001550029,0.0001018248,0.004172603,0.0000690087,0.0001575142,0.000009267796,0.0001400304,0.9905598,0.00006231034,0.001973539,0.000365665,0.0008383935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04566233,0.0005193751,0.9466081,0.0001237001,0.00609115,0.0004308765,0.00004600606,0.0001528886,0.0003655952],"genre_scores_gemma":[0.9863219,0.001063142,0.01029084,0.0001539502,0.001688914,0.000002320656,0.0001035508,0.00003464612,0.0003407188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9406596,"threshold_uncertainty_score":0.9994714,"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."}}