{"id":"W3100754158","doi":"","title":"CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discriminator; Categorical variable; Generator (circuit theory); Computer science; Regression; Benchmark (surveying); Scalar (mathematics); Algorithm; Generative adversarial network; Artificial intelligence; Mathematics; Pattern recognition (psychology); Image (mathematics); Machine learning; Statistics","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":[0.0001553303,0.0002342928,0.0002690193,0.00005628945,0.0003832991,0.0001754719,0.0005992351,0.0001129135,0.00007160774],"category_scores_gemma":[0.000086982,0.0002579081,0.0001938607,0.0004573856,0.0001114579,0.001042349,0.0001773834,0.0001427546,0.00004580513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006763457,"about_ca_system_score_gemma":0.0000916176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000130414,"about_ca_topic_score_gemma":0.00001270651,"domain_scores_codex":[0.9984129,0.0001541023,0.000199418,0.0007922846,0.00008346432,0.000357836],"domain_scores_gemma":[0.998894,0.0001401589,0.0001528015,0.0002933946,0.0002909125,0.0002287794],"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":[0.00007738677,0.00005126786,0.00007236607,0.000004961631,0.00009859299,0.00006998209,0.0002035801,0.8627915,0.004260365,0.1138981,0.01764813,0.000823819],"study_design_scores_gemma":[0.001297414,0.0001905393,0.00006104825,0.00000325394,0.00004263108,0.000002645113,0.00005081348,0.9890282,0.003152498,0.002096316,0.003770175,0.0003044808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003838164,0.00003473362,0.9933757,0.001138627,0.0005104762,0.0004027187,0.00003672138,0.000137121,0.0005257318],"genre_scores_gemma":[0.9702439,0.00002330342,0.02630189,0.00149903,0.00152263,0.000003729162,0.00009554201,0.00001620854,0.0002937822],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9670738,"threshold_uncertainty_score":0.9999873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06183486077607007,"score_gpt":0.1812116845229932,"score_spread":0.1193768237469231,"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."}}