{"id":"W3034135958","doi":"10.1109/crv50864.2020.00019","title":"Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Robustness (evolution); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Matching (statistics); Mathematics; Statistics","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":[0.0009080905,0.0001466706,0.0001663201,0.00004534893,0.00008255298,0.0002156641,0.0005169269,0.00009491439,0.0001769807],"category_scores_gemma":[0.0005107026,0.000121993,0.0000855418,0.0005052506,0.00001925721,0.0005589797,0.0001112477,0.0001852436,0.0001569846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002728804,"about_ca_system_score_gemma":0.0001597295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000292429,"about_ca_topic_score_gemma":0.00001357431,"domain_scores_codex":[0.9982564,0.0002952917,0.000168061,0.0004660596,0.000568751,0.0002454428],"domain_scores_gemma":[0.9991571,0.0001612362,0.00007220917,0.0003012356,0.0001493424,0.0001588928],"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":[0.00005888478,0.0001683324,0.005825942,0.00004145694,0.00008025449,0.00003783197,0.005660982,0.0004206535,0.02211828,0.0118212,0.01179987,0.9419663],"study_design_scores_gemma":[0.00210694,0.0009554655,0.2139743,0.00004334142,0.0000413685,0.00005105783,0.0001374703,0.7184591,0.02020774,0.008584633,0.03454535,0.0008932156],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09935468,0.00020926,0.8709878,0.01448129,0.0004358982,0.0004153516,0.000001409311,0.0006595691,0.01345469],"genre_scores_gemma":[0.9232746,0.00000641231,0.07314013,0.002852878,0.0002657617,0.00001714058,0.000006556094,0.00001011937,0.0004263651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9410731,"threshold_uncertainty_score":0.4974729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04414476406972429,"score_gpt":0.365790463255288,"score_spread":0.3216456991855637,"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."}}