{"id":"W3211003096","doi":"10.1109/iccvw54120.2021.00157","title":"Visualizing Feature Maps for Model Selection in Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Overfitting; Computer science; Convolutional neural network; Artificial intelligence; Machine learning; Feature (linguistics); Pairwise comparison; Pattern recognition (psychology); Feature selection; Deep learning; Visualization; Similarity (geometry); Model selection; Backpropagation; Artificial neural network; Image (mathematics)","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":[0.001595377,0.002193838,0.0006324192,0.002177244,0.0004910912,0.001563823,0.0009633427,0.000956103,0.003478323],"category_scores_gemma":[0.008051489,0.0005010634,0.0009525088,0.0009453169,0.0004344042,0.001629016,0.001136111,0.00148024,0.0006564501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089002,"about_ca_system_score_gemma":0.000799579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008630418,"about_ca_topic_score_gemma":0.01039691,"domain_scores_codex":[0.9996151,0.0001364757,0.00002071334,0.00007874507,0.000100454,0.00004854055],"domain_scores_gemma":[0.9984402,0.0009786938,0.0001412844,0.0001428141,0.0002418278,0.00005510587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003207498,0.0001249679,0.005035733,0.0002957124,0.0001192471,0.0003609647,0.0003460819,0.7700481,0.01405906,0.01404363,0.01324117,0.1820046],"study_design_scores_gemma":[0.000008200736,0.00001846808,0.0003804744,0.0000192586,0.000006664297,0.0000223516,0.00002749742,0.9881809,0.003074687,0.007388793,0.000863952,0.000008748915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09392208,0.000946486,0.8890156,0.001077704,0.0001159066,0.0001197719,0.001569059,0.01081554,0.002417871],"genre_scores_gemma":[0.6599046,0.0006070458,0.3333731,0.0001832051,0.00004613097,0.0003221768,0.002517632,0.001164352,0.001881815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008630418,"threshold_uncertainty_score":0.01716036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02136793863658682,"score_gpt":0.2363501487253502,"score_spread":0.2149822100887634,"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."}}