{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007524632,0.00007066972,0.00008124138,0.00001924309,0.00004069923,0.0001737057,0.000293481,0.00002705989,0.0006441666],"category_scores_gemma":[0.000008083415,0.00005672178,0.00003751044,0.00009034493,0.000008135418,0.0005952474,0.00009827669,0.00003783365,0.001497825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001070225,"about_ca_system_score_gemma":0.00001561857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277064,"about_ca_topic_score_gemma":0.00001713853,"domain_scores_codex":[0.9993849,0.00004030462,0.00009197945,0.000251981,0.0001107387,0.0001200543],"domain_scores_gemma":[0.9995062,0.00002872381,0.0000265882,0.0003596182,0.00004169452,0.00003718639],"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":[0.000004243194,0.00006877854,0.001845264,0.000001886751,0.00005193322,0.000007757082,0.0004881556,0.003716728,0.8021799,0.07175753,0.07248873,0.04738914],"study_design_scores_gemma":[0.0001519379,0.00002066103,0.001624655,0.000001664386,0.000002110845,4.209043e-7,0.000009439588,0.9525619,0.03450599,0.0008849163,0.01011864,0.0001176794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02902628,0.0000346358,0.9474909,0.0008342562,0.0006113667,0.00007766371,0.000001456121,0.00007673203,0.02184671],"genre_scores_gemma":[0.6712181,0.00000293427,0.3257136,0.000585443,0.0002569468,0.00000228889,0.000005999885,0.00000377495,0.002210894],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9488451,"threshold_uncertainty_score":0.9992796,"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."}}