{"id":"W3108330873","doi":"10.1109/cvpr46437.2021.00785","title":"HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Histogram; Color histogram; Color normalization; Computer vision; Histogram equalization; Feature (linguistics); Color image; Focus (optics); Image (mathematics); Semantics (computer science); Histogram matching; Pattern recognition (psychology); Image processing","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":[0.0005045704,0.0007552232,0.0002829341,0.0002353565,0.0001730628,0.0005698063,0.0007960245,0.0004178581,0.003024595],"category_scores_gemma":[0.001826875,0.0002745986,0.0003148979,0.0001602014,0.0006460139,0.0006912941,0.0008087893,0.001173344,0.0005076414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005438158,"about_ca_system_score_gemma":0.0002865097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009644773,"about_ca_topic_score_gemma":0.001748263,"domain_scores_codex":[0.9998112,0.00005352884,0.000005195186,0.0000629069,0.00004555637,0.00002159626],"domain_scores_gemma":[0.9996112,0.0001830707,0.00003768241,0.00009109125,0.00005060905,0.00002634487],"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":[0.0002246264,0.0001003085,0.0012619,0.0001092952,0.00006748943,0.0001417549,0.0001414196,0.7654553,0.07526458,0.03491233,0.004737808,0.1175832],"study_design_scores_gemma":[0.00001378984,0.00002939998,0.0001890318,0.000008935902,0.000008211799,0.00004124451,0.000007601934,0.9759668,0.01340688,0.008238222,0.00208018,0.000009789752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02652944,0.0001817555,0.9655818,0.0001628944,0.00008747829,0.00005885108,0.0000914292,0.001567719,0.00573869],"genre_scores_gemma":[0.7711378,0.0002356461,0.2189644,0.0004156389,0.00004677356,0.0001790556,0.0002110634,0.0009566174,0.00785303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003024595,"threshold_uncertainty_score":0.01011831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331292767048981,"score_gpt":0.2250919023878772,"score_spread":0.2117789747173874,"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."}}