{"id":"W4402702944","doi":"10.1109/mwscas60917.2024.10658738","title":"Scattering Representation and Attention-Based Residual Learning for Image Classification","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Residual; Artificial intelligence; Representation (politics); Contextual image classification; Pattern recognition (psychology); Image (mathematics); Machine learning; Computer vision; Algorithm","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":[0.0005540206,0.0007599497,0.000491388,0.0007231478,0.000218303,0.0004746803,0.001148157,0.0005869502,0.001786602],"category_scores_gemma":[0.001366471,0.0002009132,0.0006080928,0.0006814162,0.0005826101,0.00133021,0.0009031998,0.0008997344,0.0005451137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005455449,"about_ca_system_score_gemma":0.0004746529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003019163,"about_ca_topic_score_gemma":0.003807465,"domain_scores_codex":[0.9997566,0.00005495156,0.00001229507,0.00006912336,0.00006594137,0.00004110904],"domain_scores_gemma":[0.9996619,0.0001077543,0.00004254924,0.0000647572,0.0001012528,0.00002178947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002186736,0.0001860441,0.001479763,0.000167435,0.0001338513,0.0001416981,0.0001818036,0.2325802,0.0673475,0.04185428,0.004377655,0.6513311],"study_design_scores_gemma":[0.000005904865,0.00006919857,0.0002903419,0.000005440439,0.00001884177,0.00003246502,0.00001287188,0.9808397,0.007614827,0.01021235,0.000890334,0.000007774022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02931193,0.0002869894,0.9674868,0.0001869008,0.00005287862,0.00003126069,0.00006070055,0.0006893348,0.001893179],"genre_scores_gemma":[0.7839426,0.0004949176,0.2074055,0.0002536664,0.0001778195,0.0000924959,0.0005981167,0.0001519867,0.006882916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003019163,"threshold_uncertainty_score":0.006003141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867477699487824,"score_gpt":0.3501023710665167,"score_spread":0.3114275940716384,"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."}}