{"id":"W4389510032","doi":"10.1049/cvi2.12260","title":"Deep network with double reuses and convolutional shortcuts","year":2023,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Feature (linguistics); Reuse; Benchmark (surveying); Convolutional code; Artificial intelligence; Pascal (unit); Convolution (computer science); Pattern recognition (psychology); Deep learning; Algorithm; Decoding methods; Artificial neural network; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004224682,0.001329958,0.000883955,0.0006509775,0.0002830569,0.0006958519,0.001923751,0.001015189,0.002813399],"category_scores_gemma":[0.001227585,0.0004948261,0.0007849954,0.0006644445,0.0006203585,0.001835052,0.001625444,0.001327028,0.0007229511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009786829,"about_ca_system_score_gemma":0.001147141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006437974,"about_ca_topic_score_gemma":0.009439664,"domain_scores_codex":[0.9995027,0.00004872338,0.00003211788,0.0001608196,0.0001630321,0.00009263266],"domain_scores_gemma":[0.9996087,0.00006540917,0.00004825159,0.0001222664,0.0001060018,0.00004929007],"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":[0.0005132624,0.0002672375,0.001603553,0.0002494712,0.0002906823,0.0003373534,0.00008144145,0.2646657,0.05300556,0.02011856,0.01273729,0.6461299],"study_design_scores_gemma":[0.00003258944,0.0001458211,0.0003608243,0.00001222047,0.00005180114,0.00008514043,0.000006998237,0.9715986,0.01584419,0.008091189,0.003753334,0.00001728358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0687851,0.001764656,0.9179552,0.0003715584,0.0002225384,0.00009156809,0.0003993764,0.005871434,0.00453846],"genre_scores_gemma":[0.7387112,0.0006118859,0.2456934,0.0004528094,0.00009927787,0.0001456884,0.001684426,0.0003144752,0.0122868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006437974,"threshold_uncertainty_score":0.01280105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162655516393827,"score_gpt":0.2693600447507253,"score_spread":0.2530944931113426,"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."}}