{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009216483,0.0006898436,0.0006433934,0.0003833263,0.0001575394,0.0006567824,0.0008013109,0.001009681,0.002031516],"category_scores_gemma":[0.002856978,0.000445554,0.0008369571,0.0002619202,0.0007569841,0.00107303,0.0006579589,0.001335274,0.0003925766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475772,"about_ca_system_score_gemma":0.0003517897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226236,"about_ca_topic_score_gemma":0.001424017,"domain_scores_codex":[0.9997715,0.0000693524,0.000009524953,0.00006206839,0.0000540605,0.0000333595],"domain_scores_gemma":[0.9991214,0.0004896593,0.00008470137,0.000199969,0.00005580965,0.0000484136],"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":[0.00008999759,0.00006007484,0.0004858603,0.0000606734,0.00003852541,0.0001547303,0.00006516399,0.9313452,0.02653379,0.0129166,0.0006875188,0.02756179],"study_design_scores_gemma":[0.000006839492,0.00002303394,0.0001210473,0.000004810945,0.000004311253,0.00005471179,0.000004305657,0.9924076,0.003611068,0.003496622,0.0002592916,0.000006484375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07893443,0.0001294822,0.9181778,0.0002799084,0.00004623452,0.00005633814,0.00009769067,0.0006920029,0.001586165],"genre_scores_gemma":[0.7445877,0.0001866849,0.2525886,0.0003078707,0.00003670208,0.00009328208,0.000264125,0.0002340623,0.001701038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002031516,"threshold_uncertainty_score":0.006796062,"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."}}