{"id":"W2806244818","doi":"10.1109/access.2018.2842202","title":"Evolving Convolutional Neural Network and Its Application in Fine-Grained Visual Categorization","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Categorization; Classifier (UML); Pattern recognition (psychology); Class (philosophy); Deep neural networks; Machine learning; Deep learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000789869,0.0007410658,0.0005853159,0.001437575,0.0002962078,0.0005545109,0.0009788405,0.0008671783,0.0008841699],"category_scores_gemma":[0.001859385,0.0002624362,0.0005084237,0.001643197,0.0004361348,0.001142204,0.0005101616,0.0007414483,0.0002949318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164022,"about_ca_system_score_gemma":0.0006618264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02036207,"about_ca_topic_score_gemma":0.01433158,"domain_scores_codex":[0.9995221,0.00007071117,0.00003221341,0.0001907182,0.000123889,0.00006040165],"domain_scores_gemma":[0.9994633,0.0001411628,0.00006430129,0.00009798065,0.0002031616,0.00003014393],"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":[0.0001666472,0.0001492335,0.005435549,0.0001365909,0.0001319147,0.0002057776,0.00008333025,0.2047402,0.02867576,0.006827172,0.005536152,0.7479115],"study_design_scores_gemma":[0.00000393472,0.00002384743,0.001213402,0.000006730425,0.00001999872,0.00005813129,0.00001051886,0.9895383,0.005230241,0.002465589,0.001420109,0.000009275974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1508986,0.006480209,0.8311399,0.0007146025,0.0002996748,0.0001383751,0.000583672,0.00287795,0.006866938],"genre_scores_gemma":[0.8292586,0.002053624,0.160795,0.0003004944,0.0001111713,0.00007498953,0.001227765,0.00009571487,0.006082514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02036207,"threshold_uncertainty_score":0.04048711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339973708229362,"score_gpt":0.3365860451156487,"score_spread":0.313186308033355,"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."}}