{"id":"W2120001410","doi":"10.1109/icip.2008.4712326","title":"A nonlocal-means approach to exemplar-based inpainting","year":2008,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inpainting; Artificial intelligence; Image (mathematics); Similarity (geometry); Computer science; Sample (material); Pixel; Computer vision; Pattern recognition (psychology); Function (biology)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002421276,0.0001361833,0.0001542855,0.00009057579,0.0002540078,0.00008268146,0.0005856166,0.0000402642,0.00002126961],"category_scores_gemma":[0.00006196495,0.0001131733,0.00007495664,0.0005181522,0.00002747785,0.000248879,0.0001576911,0.00008123367,0.0001115318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002417842,"about_ca_system_score_gemma":0.00006648376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005461442,"about_ca_topic_score_gemma":0.000005561803,"domain_scores_codex":[0.9987956,0.00007752056,0.000168157,0.0004098119,0.0002223477,0.0003265166],"domain_scores_gemma":[0.9992227,0.00006354305,0.00003228735,0.0004531293,0.00007779669,0.000150576],"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":[0.0000415598,0.001255061,0.002605035,0.0000431773,0.0001045111,0.0001235486,0.005390985,0.6349936,0.0190142,0.04318862,0.1410168,0.1522229],"study_design_scores_gemma":[0.0001862458,0.00004514173,0.0003480221,0.000005874189,0.000002035101,0.00001077346,0.00002976336,0.9765035,0.009508025,0.00004250414,0.01311671,0.0002014332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009385793,0.00001129589,0.9449248,0.001149479,0.0001245024,0.0001490251,3.977702e-7,0.0001641063,0.05253781],"genre_scores_gemma":[0.5148762,5.983322e-7,0.4826961,0.001844927,0.0000862404,0.0000129274,6.955739e-7,0.00000592294,0.0004762789],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5139377,"threshold_uncertainty_score":0.4615071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0276241604022311,"score_gpt":0.2148781973603699,"score_spread":0.1872540369581388,"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."}}