{"id":"W4311921845","doi":"10.36227/techrxiv.21746237.v1","title":"A Comprehensive Literature Review on Convolutional Neural Networks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Intuition; Perceptron; Architecture; Visual processing; Artificial neural network; Deep learning; Visual cortex; Machine learning; Cognitive science; Perception; Psychology; Neuroscience","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":[0.0001941449,0.0002872372,0.0003609218,0.0001032256,0.0001520909,0.0002434972,0.001587121,0.0001602142,0.0003507652],"category_scores_gemma":[0.00002466544,0.0002367943,0.000256135,0.0004970406,0.00004651835,0.0001277322,0.002009614,0.001398076,0.00001862558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001146716,"about_ca_system_score_gemma":0.00009192767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003493596,"about_ca_topic_score_gemma":1.030473e-7,"domain_scores_codex":[0.997977,0.0002429006,0.0003459354,0.0007168945,0.0004910844,0.0002261659],"domain_scores_gemma":[0.9981909,0.0001097798,0.0002241644,0.0010912,0.0003029093,0.00008102297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003391501,0.0003594451,0.00003368149,0.004744546,0.000136313,0.0002358286,0.0001559337,0.001739257,0.00004710432,0.5198419,0.3303766,0.1422954],"study_design_scores_gemma":[0.0001923761,0.0002078468,0.0007657388,0.004271963,0.0000292667,0.0001086576,0.000005880217,0.5956174,0.0001230628,0.007936493,0.3898019,0.0009393977],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.000008273216,0.1339175,0.8442363,0.01152826,0.001295663,0.000973536,0.00003654729,0.001025741,0.006978177],"genre_scores_gemma":[0.05650857,0.5402142,0.1292345,0.2328561,0.001953653,0.003152564,0.003549324,0.0001688137,0.0323622],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7150018,"threshold_uncertainty_score":0.965619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224464437296518,"score_gpt":0.2976395119556144,"score_spread":0.2653948675826493,"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."}}