{"id":"W4408237370","doi":"10.23977/acss.2025.090106","title":"PGGAN: Probability Guided Generative Adversarial Network for Image Inpainting","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inpainting; Adversarial system; Image (mathematics); Generative grammar; Artificial intelligence; Computer science; Generative adversarial network; Pattern recognition (psychology); Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001400899,0.0002684697,0.0005370207,0.0001017647,0.0002902265,0.0004370041,0.0005303237,0.00009233812,0.000001639553],"category_scores_gemma":[0.00008404288,0.0002346448,0.0001008113,0.0004816089,0.00009244514,0.001031011,0.0003180327,0.000126099,0.000001252821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006212111,"about_ca_system_score_gemma":0.00007018019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004941161,"about_ca_topic_score_gemma":0.00004482915,"domain_scores_codex":[0.9975047,0.0004046056,0.0006664314,0.000773997,0.0001650856,0.0004851442],"domain_scores_gemma":[0.9983332,0.0007451499,0.0002014906,0.0004117961,0.0002385982,0.00006977269],"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":[0.00004176268,0.00008586384,0.001878676,0.000283505,0.0000849773,0.00001170884,0.0005682178,0.8107559,0.0005370202,0.06946822,0.006514191,0.1097699],"study_design_scores_gemma":[0.000765594,0.00009964614,0.0001891614,0.0002415152,0.000009182384,0.000004112508,0.00002089145,0.9638809,0.0003450447,0.02031345,0.01386899,0.0002614998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001207774,0.005032396,0.9885594,0.0004098027,0.003099167,0.001058626,0.000005978503,0.00007423799,0.0005526152],"genre_scores_gemma":[0.3818446,0.0002077537,0.6154402,0.0005516125,0.001589605,0.000240698,0.000006491861,0.00001357894,0.000105459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3806369,"threshold_uncertainty_score":0.9568536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932662524091518,"score_gpt":0.2775767608863517,"score_spread":0.2582501356454365,"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."}}