{"id":"W4384261910","doi":"10.48550/arxiv.2307.05616","title":"Image Reconstruction using Enhanced Vision Transformer","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; University of Toronto","keywords":"Artificial intelligence; Computer science; Inpainting; Computer vision; Deblurring; Transformer; Noise reduction; Benchmark (surveying); Image restoration; Image (mathematics); Image processing; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003479283,0.0006389198,0.0005491176,0.0003869236,0.0001498083,0.0006619435,0.001238102,0.0007388354,0.002775543],"category_scores_gemma":[0.0007627193,0.00028876,0.0007515161,0.0002959472,0.000499021,0.001074846,0.001021336,0.001058094,0.001149411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005760336,"about_ca_system_score_gemma":0.0005180525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002309438,"about_ca_topic_score_gemma":0.002518482,"domain_scores_codex":[0.9998078,0.00002604632,0.000006962028,0.00006181793,0.00006988861,0.00002760111],"domain_scores_gemma":[0.9998243,0.00003732131,0.00002256969,0.00005206541,0.0000445633,0.0000192467],"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":[0.0004022994,0.0002069581,0.001002035,0.0001418728,0.0001058775,0.0002858953,0.00009551577,0.5479348,0.0836105,0.02034241,0.004714703,0.3411571],"study_design_scores_gemma":[0.000007005746,0.00005934237,0.0001010782,0.000004874794,0.000009232428,0.0001224287,0.000006452844,0.9856964,0.01023369,0.002654355,0.001099541,0.000005527344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02553358,0.0002994888,0.968211,0.0001548498,0.00006235734,0.00004200315,0.00008281499,0.001816759,0.003797143],"genre_scores_gemma":[0.7281433,0.0005067444,0.2594643,0.0003596968,0.00005113126,0.00005875963,0.0004352485,0.0002564191,0.01072442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002775543,"threshold_uncertainty_score":0.009285152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08413204234903895,"score_gpt":0.2398428473043929,"score_spread":0.1557108049553539,"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."}}