{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001847911,0.001557048,0.001307603,0.001130461,0.0003742026,0.001739582,0.001661011,0.001803731,0.006641588],"category_scores_gemma":[0.004646922,0.0007362562,0.001291331,0.001801772,0.001084034,0.002418848,0.001515405,0.002884399,0.002739042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193432,"about_ca_system_score_gemma":0.0009012256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001917702,"about_ca_topic_score_gemma":0.001359539,"domain_scores_codex":[0.9987955,0.0004056811,0.00007942748,0.000218403,0.000431716,0.00006925278],"domain_scores_gemma":[0.9975814,0.001884339,0.00006753758,0.000189917,0.0002355262,0.00004126293],"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":[0.00009892837,0.0001069453,0.001281007,0.001714034,0.0002303901,0.0002032918,0.0001474906,0.2095131,0.001509614,0.227726,0.03581513,0.5216541],"study_design_scores_gemma":[0.00003030497,0.0001389505,0.001133763,0.001256237,0.0001087012,0.0008232932,0.00007139197,0.4045416,0.003258109,0.2646683,0.3238718,0.00009758116],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.003269647,0.1571703,0.7656367,0.004194858,0.001369166,0.0001366047,0.0006412902,0.0007427583,0.06683876],"genre_scores_gemma":[0.2402824,0.4358602,0.2603126,0.004813777,0.005023258,0.0006985217,0.003000472,0.001117124,0.04889156],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006641588,"threshold_uncertainty_score":0.02221835,"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."}}