{"id":"W4312810855","doi":"10.1109/cvpr52688.2022.00878","title":"Styleformer: Transformer based Generative Adversarial Networks with Style Vector","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":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Computer science; Transformer; Normalization (sociology); Artificial intelligence; Visualization; Computation; Generative grammar; Generator (circuit theory); Computer vision; Algorithm; Voltage; Engineering","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":[0.0006849882,0.001064579,0.0005176004,0.0004058871,0.0001959585,0.0006473051,0.001394847,0.0007659438,0.004089567],"category_scores_gemma":[0.001540888,0.0004572344,0.000774534,0.0003681514,0.0006205625,0.0009326768,0.001167042,0.001292716,0.00120457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006181113,"about_ca_system_score_gemma":0.0004274079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145494,"about_ca_topic_score_gemma":0.002442088,"domain_scores_codex":[0.9997262,0.00006935475,0.000009794135,0.00008698934,0.00007609867,0.00003146593],"domain_scores_gemma":[0.9996477,0.0001464288,0.00003554953,0.00008559706,0.00005466908,0.00003006643],"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":[0.0001840476,0.00008488337,0.00111535,0.0001204486,0.0001291135,0.0001896774,0.0001017138,0.6673842,0.02794043,0.02491128,0.007401933,0.2704368],"study_design_scores_gemma":[0.000008375123,0.0000288656,0.0000703573,0.000004864979,0.000008061524,0.00004306404,0.000003159366,0.9896701,0.003983848,0.004968614,0.00120454,0.000006166848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006087583,0.0001806022,0.9896004,0.0001218256,0.00006696991,0.00004694609,0.0001000714,0.001465362,0.002330276],"genre_scores_gemma":[0.5403035,0.0004762266,0.4427105,0.0008576295,0.0001240027,0.0002655687,0.0007122417,0.0007121052,0.0138382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004089567,"threshold_uncertainty_score":0.01368093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206577613607771,"score_gpt":0.2293954717748598,"score_spread":0.2073296956387821,"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."}}