{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002903483,0.001105012,0.001078396,0.000768118,0.0003821485,0.001292867,0.001994558,0.001717711,0.00297562],"category_scores_gemma":[0.008974612,0.0009091032,0.00122569,0.0007526564,0.002307665,0.001961169,0.002259124,0.003340202,0.000614443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557592,"about_ca_system_score_gemma":0.000974449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00366462,"about_ca_topic_score_gemma":0.004193509,"domain_scores_codex":[0.9990582,0.0004320557,0.00003108574,0.0002468164,0.0001539427,0.0000778788],"domain_scores_gemma":[0.9958844,0.003073588,0.0002769354,0.0004587516,0.0001730573,0.0001332503],"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":[0.00007373363,0.00003818289,0.0003671938,0.00006107605,0.00004639563,0.00005701651,0.00006547457,0.9380626,0.001718902,0.03846184,0.0008432689,0.02020418],"study_design_scores_gemma":[0.000005272961,0.00001068306,0.00003506075,0.00000447318,0.000003650481,0.00001252128,0.000002831066,0.9743131,0.0004716759,0.02491231,0.0002243987,0.000003990212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007542924,0.000174882,0.9908824,0.0002669614,0.00001871821,0.00002528293,0.00007671006,0.0003117656,0.0007004305],"genre_scores_gemma":[0.5926282,0.0004821298,0.3982381,0.0005032058,0.0001777746,0.0002363733,0.0006307107,0.0004150982,0.0066885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00366462,"threshold_uncertainty_score":0.01535523,"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."}}