{"id":"W4298289240","doi":"10.48550/arxiv.1406.2661","title":"Generative Adversarial Networks","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":4572,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Discriminative model; Minimax; Computer science; Inference; Artificial intelligence; Perceptron; Generative grammar; Machine learning; Sample (material); Generative model; Mistake; Artificial neural network; Mathematical optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009307186,0.001565541,0.001018943,0.0006003887,0.0003428184,0.0009718623,0.001465475,0.00144477,0.005735756],"category_scores_gemma":[0.005800062,0.0006794384,0.0008320137,0.0006252329,0.001007225,0.001237823,0.001770082,0.00267245,0.001842212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098731,"about_ca_system_score_gemma":0.0006231082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003420462,"about_ca_topic_score_gemma":0.005796148,"domain_scores_codex":[0.9993953,0.0002300311,0.00002097205,0.000183783,0.0001030331,0.0000669026],"domain_scores_gemma":[0.9974044,0.002049966,0.0001050583,0.000223892,0.0001636647,0.0000530416],"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":[0.00007804581,0.00003073009,0.0004247955,0.00006427629,0.00005358544,0.00006793319,0.00004338687,0.9289355,0.001569602,0.02253367,0.003669332,0.04252914],"study_design_scores_gemma":[0.000004022742,0.000007446072,0.00002744708,0.000005024623,0.000003458567,0.000009755414,0.00000309517,0.9898469,0.0004776756,0.009151993,0.0004596422,0.000003511705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0127714,0.000541133,0.9794997,0.0005561168,0.000105601,0.00006626802,0.0004124441,0.001916348,0.004130914],"genre_scores_gemma":[0.6982449,0.0006991913,0.275828,0.001054581,0.0002442153,0.000448188,0.002007052,0.0007756529,0.0206982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005735756,"threshold_uncertainty_score":0.01918799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0456119184112788,"score_gpt":0.1738836741732009,"score_spread":0.1282717557619221,"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."}}