{"id":"W4393132109","doi":"10.1016/j.eswa.2024.123709","title":"OENet: An overexposure correction network fused with residual block and transformer","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Residual; Computer science; Transformer; Block (permutation group theory); Algorithm; Electrical engineering; Mathematics; Voltage","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":[0.0004046394,0.0007636077,0.0004837314,0.0004822,0.000233896,0.000419151,0.0009009701,0.0006575167,0.00430842],"category_scores_gemma":[0.000662494,0.0003025179,0.0004059927,0.0003936425,0.0002184286,0.0008275085,0.00076463,0.0007496343,0.001219548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003681557,"about_ca_system_score_gemma":0.0006023969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006652025,"about_ca_topic_score_gemma":0.01250555,"domain_scores_codex":[0.9998643,0.00001197075,0.000005947839,0.00004027468,0.00005743988,0.00001995103],"domain_scores_gemma":[0.9998423,0.00002912666,0.00001304679,0.00003019689,0.00007540779,0.000009935188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000530885,0.000137275,0.00153636,0.0001308145,0.0001597997,0.0001824474,0.00004145364,0.1183789,0.06670712,0.002732852,0.009871664,0.7995904],"study_design_scores_gemma":[0.00002216393,0.00009986498,0.0009975509,0.00001508251,0.00006866186,0.0001625773,0.00001341723,0.9391968,0.05164611,0.001510193,0.006245516,0.00002192067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02581763,0.0003857167,0.9643784,0.0001146138,0.0002253194,0.0000557046,0.000363127,0.005663508,0.002996024],"genre_scores_gemma":[0.4710096,0.0004068889,0.5055689,0.0003428234,0.000102695,0.00007243086,0.001584879,0.000581806,0.02033002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006652025,"threshold_uncertainty_score":0.01441312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013189593889547,"score_gpt":0.2470492264960724,"score_spread":0.2369173305571769,"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."}}