{"id":"W4390189607","doi":"10.1109/iccvw60793.2023.00142","title":"MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Upsampling; Computer science; Image restoration; FLOPS; Image quality; Mobile device; Convolution (computer science); Image (mathematics); Code (set theory); Decoupling (probability); Software deployment; Artificial intelligence; Computer engineering; Algorithm; Computer vision; Parallel computing; Image processing; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006102397,0.001632993,0.001058905,0.001356468,0.0005220972,0.001319301,0.001914457,0.001201387,0.007801123],"category_scores_gemma":[0.002481121,0.0007979403,0.001873243,0.0006628872,0.0004262989,0.001480116,0.001996645,0.0025314,0.003548024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938618,"about_ca_system_score_gemma":0.001625032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009835172,"about_ca_topic_score_gemma":0.01604018,"domain_scores_codex":[0.9996823,0.00003641191,0.00001862134,0.00006857223,0.000152464,0.00004173873],"domain_scores_gemma":[0.9995054,0.0001406648,0.00004238141,0.0001281897,0.0001495792,0.00003380113],"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":[0.0001864014,0.0001197898,0.000823013,0.0003609737,0.0001518982,0.0002116643,0.0001515427,0.3564268,0.01918069,0.008096374,0.03180449,0.5824863],"study_design_scores_gemma":[0.00001499192,0.00003932742,0.0001298057,0.00002503045,0.0000165503,0.0001183385,0.0000210201,0.9799128,0.004722708,0.004853886,0.0101272,0.00001833834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003185984,0.0005005479,0.9847317,0.0001849727,0.00006997698,0.000090766,0.0002724605,0.009612224,0.001351439],"genre_scores_gemma":[0.06523281,0.000715415,0.92521,0.0002653417,0.00005511408,0.000292648,0.001690954,0.001614012,0.004923848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009835172,"threshold_uncertainty_score":0.02609736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04163696323873681,"score_gpt":0.35633388891703,"score_spread":0.3146969256782932,"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."}}