{"id":"W3003244550","doi":"10.1109/access.2020.2970169","title":"Image Inpainting Based on Generative Adversarial Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"State Key Laboratory of Ocean Engineering; Shanghai Jiao Tong University; Yangzhou University; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Discriminator; Inpainting; Computer science; Generator (circuit theory); Artificial intelligence; Encoder; Image (mathematics); Context (archaeology); Pixel; Pattern recognition (psychology); Generative grammar; Consistency (knowledge bases); Computer vision; Power (physics)","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":[0.000581319,0.0009020744,0.0007165577,0.0003621072,0.0001975057,0.0004482577,0.0009549017,0.0007452669,0.001963172],"category_scores_gemma":[0.001387407,0.0003933511,0.0006005441,0.0002831492,0.0006879725,0.0005673781,0.0007393002,0.001536524,0.0003540149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006777831,"about_ca_system_score_gemma":0.0003618158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002241712,"about_ca_topic_score_gemma":0.002897595,"domain_scores_codex":[0.999778,0.00005920296,0.000006987142,0.00006699157,0.00005900292,0.00002982166],"domain_scores_gemma":[0.9994527,0.0003556626,0.00005717902,0.00005116235,0.00005738836,0.00002590315],"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":[0.00004410214,0.00002067008,0.0002111582,0.00003407488,0.00002207168,0.00007031431,0.0000301706,0.9673412,0.003147198,0.004400208,0.0009504257,0.02372844],"study_design_scores_gemma":[0.00000249801,0.000009243552,0.00002711127,0.000002067999,0.000002350233,0.00001699133,0.000001401269,0.9977204,0.0005484414,0.001510222,0.0001574807,0.000001706399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0223145,0.0004846835,0.973332,0.0002512225,0.00005249835,0.00005069584,0.00009661229,0.0006820593,0.002735633],"genre_scores_gemma":[0.8612564,0.0005273279,0.1287691,0.0003249352,0.0000837824,0.0001328347,0.0003968816,0.0001687143,0.008339963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002241712,"threshold_uncertainty_score":0.006567478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103798703353154,"score_gpt":0.267500521162605,"score_spread":0.2364625341290734,"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."}}