{"id":"W2965833116","doi":"10.1109/cvpr.2019.00878","title":"Image Generation From Layout","year":2019,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":215,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Embedding; Bounding overwatch; Set (abstract data type); Object (grammar); Image (mathematics); Boosting (machine learning); Representation (politics); Pattern recognition (psychology); Generative model; Encoding (memory); Generative grammar; Computer vision","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":[0.0003426671,0.0009660195,0.000532156,0.0004169418,0.0001874829,0.0005884475,0.001187525,0.0008443177,0.005399246],"category_scores_gemma":[0.001300337,0.000408611,0.0007932482,0.0003375845,0.0004959889,0.0007453614,0.001046084,0.001099591,0.001641243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007121534,"about_ca_system_score_gemma":0.0004103458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00130779,"about_ca_topic_score_gemma":0.002178347,"domain_scores_codex":[0.9997892,0.00003341877,0.000007338224,0.00007715749,0.00006583581,0.00002704593],"domain_scores_gemma":[0.9996494,0.0001463969,0.00002739041,0.00009351405,0.00005977717,0.0000234931],"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":[0.0002433565,0.00008961271,0.001061,0.0002918384,0.00007918149,0.0004261898,0.0001353175,0.6303083,0.05040214,0.02648638,0.01661658,0.2738601],"study_design_scores_gemma":[0.00001735885,0.00003425481,0.000132292,0.00001159511,0.000009959661,0.0001367765,0.00001024995,0.9758075,0.01152741,0.00869558,0.003607576,0.000009455896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01881712,0.0005751101,0.9694866,0.000386977,0.0001999669,0.0001348212,0.000569504,0.004153366,0.005676442],"genre_scores_gemma":[0.56876,0.0006978624,0.4094468,0.0007605642,0.0001264304,0.000343757,0.002869576,0.001271667,0.01572328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005399246,"threshold_uncertainty_score":0.01806229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234276547243436,"score_gpt":0.2103868907219398,"score_spread":0.1980441252495055,"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."}}