{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008463322,0.001108266,0.0006675069,0.0004297612,0.0002065593,0.000665565,0.001450574,0.0009696675,0.003400542],"category_scores_gemma":[0.002275278,0.0003409137,0.0006102403,0.0004153756,0.0008534612,0.001124805,0.001402152,0.002237017,0.001094374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00075711,"about_ca_system_score_gemma":0.0005884618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001910645,"about_ca_topic_score_gemma":0.00278554,"domain_scores_codex":[0.9995947,0.0001330379,0.00001089017,0.0001113141,0.0001073358,0.00004268851],"domain_scores_gemma":[0.9993877,0.0003586437,0.00004589698,0.0001153039,0.00006239971,0.00003002784],"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":[0.0001301758,0.00005478827,0.0005477534,0.0001120053,0.00006076522,0.000126187,0.00005150953,0.8046355,0.007133874,0.05037004,0.01175727,0.1250201],"study_design_scores_gemma":[0.00000504186,0.00001402895,0.00006043863,0.000008423983,0.000003431699,0.00003141816,0.000002323206,0.9868065,0.001188302,0.01025333,0.001621344,0.000005486265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006023619,0.0005662823,0.9873908,0.0002522415,0.00007434091,0.00004936865,0.0002567491,0.001319171,0.004067457],"genre_scores_gemma":[0.5264182,0.001178166,0.4531521,0.001095778,0.0001879436,0.0003802844,0.002450613,0.0006552032,0.01448171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003400542,"threshold_uncertainty_score":0.0113759,"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."}}