{"id":"W3124178031","doi":"10.15353/jcvis.v6i1.3536","title":"Pal-GAN: Palette-conditioned Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Palette (painting); Artificial intelligence; Adversarial system; Generative grammar; Image translation; Intersection (aeronautics); Image (mathematics); Variety (cybernetics); Segmentation; Discriminative model; Class (philosophy); Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000567938,0.000169973,0.0003515391,0.000137427,0.0002576917,0.0006442873,0.0002357785,0.00004373592,0.00001822791],"category_scores_gemma":[0.00009135058,0.000139566,0.0001357947,0.0002808792,0.0000606242,0.0009733037,0.00008656969,0.0002009602,0.000004754019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000433011,"about_ca_system_score_gemma":0.0001714092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004910489,"about_ca_topic_score_gemma":6.117872e-7,"domain_scores_codex":[0.9980805,0.000395914,0.0006218975,0.000223719,0.000485719,0.000192233],"domain_scores_gemma":[0.9979049,0.0003641706,0.0004791743,0.0001475664,0.0009542822,0.000149936],"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":[0.00002293891,0.0000782986,0.0005024794,0.0000140655,0.0001172126,0.0002138825,0.0004144383,0.9580392,0.001349016,0.007819325,0.01829939,0.01312978],"study_design_scores_gemma":[0.0009618646,0.00007138379,0.003719377,0.0001286243,0.00002197628,0.0007088811,0.000191859,0.9862103,0.0002213513,0.002261738,0.005326173,0.000176488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002775222,0.002522783,0.9887518,0.003198103,0.002337564,0.00007447905,0.000003468901,0.00001947068,0.0003170616],"genre_scores_gemma":[0.9628324,0.00006964081,0.03497737,0.00086676,0.001148333,0.000001581519,0.00001010247,0.000009778372,0.00008403975],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9600572,"threshold_uncertainty_score":0.6212878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006727752864173138,"score_gpt":0.2368859368188145,"score_spread":0.2301581839546414,"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."}}