{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002345427,0.000338352,0.000327825,0.0004326113,0.0002337629,0.0002126175,0.001455228,0.0002990754,0.00001245414],"category_scores_gemma":[0.00003887012,0.0004103389,0.0001924125,0.0008438491,0.0001863216,0.001722571,0.0008173539,0.0006358695,0.00006670898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003447698,"about_ca_system_score_gemma":0.0002109327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005831251,"about_ca_topic_score_gemma":0.00001127556,"domain_scores_codex":[0.9978705,0.00008627254,0.0002538196,0.001295277,0.0001079719,0.000386129],"domain_scores_gemma":[0.9982598,0.00005590323,0.0002936676,0.00102821,0.0002498521,0.0001125047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002289559,0.0004086316,0.0006288844,0.001632968,0.0003599342,0.00129612,0.001833101,0.0883111,0.6950597,0.0499757,0.00066486,0.1596001],"study_design_scores_gemma":[0.0002050923,0.00003382757,0.00005820101,0.000341942,0.00003349351,0.00001484097,0.00003560728,0.7962835,0.0209964,0.1815013,0.00002912069,0.0004665897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04457716,0.00002035837,0.9517505,0.00007029931,0.0006900526,0.0002764766,0.000007456053,0.001786611,0.000821085],"genre_scores_gemma":[0.5096622,0.0001762886,0.4896395,0.00002655011,0.0000487712,0.000001196964,0.000006073532,0.0000340303,0.0004053836],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7079725,"threshold_uncertainty_score":0.9998348,"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."}}