{"id":"W3125751664","doi":"10.15353/jcvis.v6i1.3537","title":"Deep Residual Transform for Multi-scale Image Decomposition","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Computer science; Residual; Artificial intelligence; Leverage (statistics); Granularity; Transformation (genetics); Representation (politics); Pattern recognition (psychology); Hierarchy; Image (mathematics); Decomposition; Computer vision; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0004225883,0.0008850688,0.0006898525,0.0008094677,0.0001781707,0.0007216271,0.0009804729,0.0008138096,0.002966372],"category_scores_gemma":[0.001345312,0.0002712736,0.0009709164,0.001079217,0.0004888447,0.001163668,0.0009843924,0.001973297,0.001617923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005337885,"about_ca_system_score_gemma":0.0005935531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002891163,"about_ca_topic_score_gemma":0.004493559,"domain_scores_codex":[0.9997607,0.0000322974,0.0000133031,0.00006302141,0.00009710788,0.00003356739],"domain_scores_gemma":[0.999728,0.00008375901,0.00004024577,0.00006327673,0.00006250221,0.00002223621],"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":[0.0002350227,0.0001763963,0.001154712,0.0003646663,0.0001515046,0.0003086164,0.0001304589,0.3146122,0.07414474,0.03310682,0.0150717,0.5605432],"study_design_scores_gemma":[0.000009205785,0.00003913979,0.0003095523,0.00001187031,0.00001642775,0.00007222594,0.00001567107,0.9798916,0.007446794,0.008548551,0.003628194,0.00001078217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009332314,0.0006913145,0.9867692,0.0002186979,0.00005906461,0.00003169613,0.000267111,0.001306623,0.001324004],"genre_scores_gemma":[0.3295661,0.001923942,0.6581045,0.0004120683,0.0001527766,0.0001764098,0.002871916,0.0004613274,0.006330927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002966372,"threshold_uncertainty_score":0.009923518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01968876424958521,"score_gpt":0.3453194604014362,"score_spread":0.325630696151851,"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."}}