{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004430312,0.0004004232,0.0009058905,0.0001362405,0.0001506798,0.0003195129,0.0007226918,0.0003028103,0.0000631724],"category_scores_gemma":[0.00007403176,0.0003682001,0.0002120267,0.0002703954,0.0002158772,0.0002354138,0.0009310424,0.0003350303,0.000002631032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226458,"about_ca_system_score_gemma":0.0002852357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355534,"about_ca_topic_score_gemma":0.0003002014,"domain_scores_codex":[0.9974636,0.0002947235,0.0006136242,0.0009552328,0.0003124199,0.0003603413],"domain_scores_gemma":[0.9978724,0.0002074995,0.0004328825,0.0007711842,0.0005573109,0.0001587865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002584197,0.001022565,0.002116715,0.0007658617,0.001973053,0.0004705488,0.004668323,0.1796928,0.6378053,0.006200849,0.0187814,0.1462442],"study_design_scores_gemma":[0.001115124,0.0002265017,0.0009746854,0.0001474297,0.0001713423,0.00001841192,0.0001456749,0.8568935,0.1369773,0.000574427,0.001844155,0.0009114175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02299029,0.0021043,0.9690286,0.0006057123,0.001392296,0.0004458161,0.00001398954,0.0001423841,0.00327659],"genre_scores_gemma":[0.840576,0.0006739996,0.1575327,0.0001666098,0.0001586265,0.00003707268,0.00003417895,0.00002222029,0.0007985652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8175857,"threshold_uncertainty_score":0.999877,"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."}}