{"id":"W3158196134","doi":"10.1109/iscas51556.2021.9401641","title":"MISNet: Multi-Resolution Level Feature Interpolating Ultralight-Weight Residual Image Super Resolution Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Benchmark (surveying); Feature (linguistics); Interpolation (computer graphics); Computer science; Convolutional neural network; Artificial intelligence; Image resolution; Pattern recognition (psychology); Resolution (logic); Image (mathematics); Computer vision; Algorithm; Geography; Cartography","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":[0.0002899747,0.00058821,0.0004175191,0.0003778194,0.0001757394,0.0004055148,0.001316323,0.000547081,0.002352828],"category_scores_gemma":[0.0006442874,0.0002544845,0.0004033973,0.0003726516,0.0003113336,0.001041708,0.0006187577,0.0007743635,0.0007997234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004736717,"about_ca_system_score_gemma":0.0005458187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004474697,"about_ca_topic_score_gemma":0.008677721,"domain_scores_codex":[0.999887,0.00001098095,0.000005275939,0.0000329262,0.00004397046,0.00001983685],"domain_scores_gemma":[0.9998721,0.00002794486,0.00002063527,0.0000279309,0.00004039608,0.00001104927],"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":[0.0004518997,0.0002067663,0.001503873,0.0002310557,0.0001802575,0.0003485189,0.00009608948,0.2964669,0.09949923,0.01219302,0.01501849,0.573804],"study_design_scores_gemma":[0.00001132211,0.00007295285,0.0004012276,0.000009402367,0.0000222534,0.00009375448,0.000007399354,0.9767323,0.01759419,0.001945229,0.003097314,0.00001260842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04564654,0.001129027,0.9439986,0.0002337185,0.0001402907,0.00006713713,0.0004348111,0.004574226,0.003775598],"genre_scores_gemma":[0.5677798,0.0008865092,0.4157573,0.0004636091,0.00007145268,0.0001092359,0.001737163,0.0002458929,0.01294903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004474697,"threshold_uncertainty_score":0.008897305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340662461550179,"score_gpt":0.2917380005966697,"score_spread":0.2583313759811679,"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."}}