{"id":"W4312725636","doi":"10.1109/cvpr52688.2022.01037","title":"Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Generative grammar; Artificial intelligence; Image translation; Segmentation; Image (mathematics); Domain (mathematical analysis); Translation (biology); Pattern recognition (psychology); Generative model; Computer vision; Mathematics; Biology","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0005908732,0.0004844505,0.0005084213,0.0001856596,0.001514644,0.0009793005,0.0007213835,0.00009264168,0.0005976573],"category_scores_gemma":[0.00001737751,0.0004238679,0.000134134,0.000445723,0.0001212097,0.0005816547,0.0005553489,0.0004055338,0.00007497479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006521931,"about_ca_system_score_gemma":0.00008462252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000182396,"about_ca_topic_score_gemma":0.0001534008,"domain_scores_codex":[0.9961485,0.0007432304,0.0005046657,0.001227938,0.0007342643,0.0006413815],"domain_scores_gemma":[0.9982407,0.0003383979,0.0003253845,0.0006120861,0.000224604,0.000258825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001966521,0.0003690285,0.001029329,0.0000326384,0.0001159606,0.0001853127,0.002011446,0.00746637,0.0151008,0.0001938115,0.004976901,0.9683217],"study_design_scores_gemma":[0.003131347,0.002215864,0.001762394,0.0001678725,0.00002866159,0.0001997645,0.0005216205,0.9782516,0.007571931,0.00090926,0.004115491,0.001124239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1814879,0.0000467398,0.8143302,0.002048491,0.001006138,0.0004293633,0.0002565668,0.000190989,0.0002035667],"genre_scores_gemma":[0.9692677,0.00007519536,0.02539579,0.004216788,0.0004541084,0.0001299516,0.0002484415,0.00004111058,0.0001708869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9707852,"threshold_uncertainty_score":0.9998213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05236663245838136,"score_gpt":0.267660208035971,"score_spread":0.2152935755775896,"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."}}