{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006681828,0.00007894738,0.0000924094,0.000004209921,0.0001143503,0.00004146285,0.00005297986,0.0001039218,0.0000779063],"category_scores_gemma":[0.00001524257,0.00002661465,0.00007128416,0.0002895374,0.000008464068,0.00009143106,0.0000219851,0.00009422221,0.000001840368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001971217,"about_ca_system_score_gemma":0.000005937194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002985364,"about_ca_topic_score_gemma":0.002317226,"domain_scores_codex":[0.9994099,0.00002202912,0.00009161534,0.0001978882,0.00007701352,0.0002016065],"domain_scores_gemma":[0.9997777,0.00007302575,0.00002147149,0.00001045304,0.00007889028,0.00003849977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001273923,0.0003932141,0.05571556,0.00001614568,0.00003654977,0.0000106665,0.00009325187,0.07028779,0.57278,0.03036748,0.2401942,0.02997774],"study_design_scores_gemma":[0.0003075977,0.00009075594,0.1000901,0.00001265293,0.000009919517,0.0000272028,0.0002511639,0.8726005,0.002578309,0.001671609,0.02209626,0.0002639339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888525,0.0003958198,0.001458309,0.007205011,0.0002055441,0.0002428881,0.00002396825,0.00009715059,0.001518837],"genre_scores_gemma":[0.993971,0.00001116185,0.000588325,0.001126772,0.0005394456,0.00002467253,0.0002832733,4.748788e-7,0.003454807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8023127,"threshold_uncertainty_score":0.1293066,"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."}}