{"id":"W2936485314","doi":"","title":"Survey on generative adversarial networks","year":2019,"lang":"en","type":"article","venue":"International journal of advance research, ideas and innovations in technology","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discriminator; Adversarial system; Generator (circuit theory); Computer science; Image translation; Image (mathematics); Generative grammar; Scope (computer science); Artificial intelligence; Generative adversarial network; Translation (biology); Deep learning; Theoretical computer science; Telecommunications; Programming language; Power (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001740527,0.0001082829,0.0002215218,0.001615924,0.00007766566,0.0001117976,0.00105396,0.0001146831,0.00001953703],"category_scores_gemma":[0.0013299,0.00009424506,0.00003324652,0.001692647,0.0001784519,0.0006648347,0.000323065,0.0007377026,0.000008935027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001522914,"about_ca_system_score_gemma":0.0001314414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003608164,"about_ca_topic_score_gemma":0.00005853366,"domain_scores_codex":[0.9982535,0.0002054558,0.0004895027,0.000257858,0.0005284086,0.0002652493],"domain_scores_gemma":[0.9963307,0.0004571333,0.0002389471,0.000247686,0.002687067,0.00003844241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002439123,0.0002613422,0.03770031,0.00000305448,0.0001564379,0.0001066112,0.0001307013,0.06808656,0.002608309,0.6125866,0.003230025,0.2748862],"study_design_scores_gemma":[0.006720336,0.003077964,0.1893591,0.0005743954,0.000007077774,0.0002150514,0.0005774683,0.3963379,0.00969291,0.342755,0.04981166,0.0008712219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680598,0.0007897766,0.814602,0.01245786,0.002525708,0.0002721437,0.00001010284,0.00002817272,0.001254424],"genre_scores_gemma":[0.9772516,0.0005419587,0.02161864,0.0002565515,0.0002097336,0.000005353106,0.000003614698,0.000007444188,0.0001050762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8091918,"threshold_uncertainty_score":0.3843202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670243258893162,"score_gpt":0.3453083014571076,"score_spread":0.3186058688681759,"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."}}