{"id":"W2556967412","doi":"10.48550/arxiv.1701.04128","title":"Understanding the Effective Receptive Field in Deep Convolutional Neural Networks","year":2017,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":806,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Receptive field; Computer science; Artificial intelligence; Convolutional neural network; Field (mathematics); Surround suppression; Gaussian; Dropout (neural networks); Computer vision; Pattern recognition (psychology); Machine learning; Mathematics; Visual perception; Psychology; Perception","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":[0.0002606704,0.0001255059,0.0001271305,0.00005805616,0.0009522028,0.0002017774,0.001436178,0.00007607621,0.00000802933],"category_scores_gemma":[0.00005440028,0.000100902,0.00008512741,0.0002652428,0.0001469109,0.0005580787,0.0007264792,0.0003727174,0.000006025708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498245,"about_ca_system_score_gemma":0.00001648286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009640024,"about_ca_topic_score_gemma":0.0002640437,"domain_scores_codex":[0.9989526,0.0001504744,0.00009526023,0.0004110749,0.00005721041,0.0003333987],"domain_scores_gemma":[0.9986234,0.0005590684,0.0001336073,0.0005792169,0.00003727358,0.00006739425],"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":[0.0000319069,0.00001790946,0.02199769,0.000002320718,0.00001897738,0.0001590139,0.000131427,0.7809199,0.000003417458,0.1949336,0.0001555678,0.001628247],"study_design_scores_gemma":[0.000377597,0.00005949316,0.01637915,0.00002255015,0.000004933785,0.000005862376,0.00009188938,0.9667904,0.000007096721,0.01610795,0.00002406594,0.0001289797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1086964,0.00003552618,0.8876414,0.0007499449,0.0005640707,0.0001890604,2.09854e-7,0.00004608146,0.00207729],"genre_scores_gemma":[0.9994097,0.00002106485,0.000103135,0.0002180197,0.0001215463,5.549454e-7,4.068228e-7,0.000004855644,0.0001207123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8907133,"threshold_uncertainty_score":0.732367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09902396535453362,"score_gpt":0.1995139825676963,"score_spread":0.1004900172131627,"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."}}