{"id":"W3131435758","doi":"","title":"Image Completion via Inference in Deep Generative Models","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Leverage (statistics); Computer science; Artificial intelligence; Generative grammar; Generative model; Image (mathematics); Perspective (graphical); Bayesian inference; Machine learning; Pattern recognition (psychology); Bayesian probability","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002529773,0.0003777478,0.0004897984,0.0002527848,0.0001499708,0.0003172704,0.001315292,0.0002563919,0.00005750299],"category_scores_gemma":[0.00005198899,0.0004475343,0.0002003824,0.0007280003,0.0001210091,0.001176051,0.002120494,0.0006073433,0.00002885562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002714371,"about_ca_system_score_gemma":0.0002133789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005625631,"about_ca_topic_score_gemma":0.0007867152,"domain_scores_codex":[0.9973952,0.0004716002,0.0002733858,0.001332861,0.0001199249,0.0004070421],"domain_scores_gemma":[0.9980968,0.0001742916,0.0002218432,0.00102529,0.0003431654,0.0001386067],"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":[0.000008889127,0.00008756563,0.0002385546,0.00002011899,0.00004278304,0.0003151358,0.0005454698,0.9803586,0.000602418,0.01674891,0.0000307536,0.00100083],"study_design_scores_gemma":[0.0002995833,0.00002349742,0.0006297526,0.00006901658,0.00002224591,0.000001654209,0.00009146624,0.9605528,0.0005223176,0.03732689,0.00001900103,0.0004417934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02963233,0.0001179174,0.9677265,0.000120289,0.0004554169,0.0002595506,0.000006804727,0.00009116972,0.001589997],"genre_scores_gemma":[0.9393432,0.0002404798,0.06000552,0.0001374427,0.00008833716,0.000002470126,0.00003884713,0.0000150299,0.0001287292],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9097108,"threshold_uncertainty_score":0.9997976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07680241438404604,"score_gpt":0.195674236800266,"score_spread":0.1188718224162199,"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."}}