{"id":"W2121331909","doi":"","title":"Generating more realistic images using gated MRF's","year":2010,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Inpainting; Pixel; Probabilistic logic; Pattern recognition (psychology); Set (abstract data type); Statistical model; Latent variable; Image (mathematics); Computer vision; Generative model; Image resolution; Generative grammar","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003502482,0.0001844718,0.0001912383,0.0001345795,0.0005229588,0.001875377,0.0004395431,0.00009049612,0.000004993427],"category_scores_gemma":[0.0001423694,0.0001557925,0.00004416139,0.00042426,0.00005490374,0.005645065,0.00009699883,0.0002368217,0.00001670272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003002448,"about_ca_system_score_gemma":0.00009751999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001836609,"about_ca_topic_score_gemma":0.000003675462,"domain_scores_codex":[0.9985948,0.00006383815,0.0005340939,0.0001917118,0.0003209272,0.0002945889],"domain_scores_gemma":[0.9987279,0.00003760461,0.0004029278,0.0003091898,0.0004271744,0.00009516053],"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":[0.000008625717,0.00002744644,0.0002735159,0.000345516,0.00002261335,0.000009298166,0.003292385,0.6970004,0.1304008,0.002118555,0.002773506,0.1637274],"study_design_scores_gemma":[0.0001379374,0.00001220955,0.0000857714,0.00004649424,0.00000664547,0.00007323861,0.0001187904,0.9941469,0.003357136,0.00001799258,0.00180726,0.0001896612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04006451,0.00008330918,0.956621,0.000296908,0.001641173,0.0002251445,0.000006257279,0.0002808546,0.0007808462],"genre_scores_gemma":[0.9540377,0.000001302426,0.04515413,0.0003271814,0.0003799032,0.00001037448,0.00001757689,0.000009048377,0.00006273183],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9139732,"threshold_uncertainty_score":0.9991608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859920218874686,"score_gpt":0.2607507749789137,"score_spread":0.2421515727901669,"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."}}