{"id":"W2786593326","doi":"10.1109/icip.2018.8451100","title":"Deep Feature Compression for Collaborative Object Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Lossy compression; Computer science; Lossless compression; Feature (linguistics); Overhead (engineering); Data compression; Cloud computing; Focus (optics); Mobile device; Artificial intelligence; Compression (physics); Object detection; Real-time computing; Object (grammar); Computer vision; Computer engineering; Pattern recognition (psychology)","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":[0.00009739154,0.0002632698,0.0002476398,0.00008747989,0.0003112261,0.0001760311,0.001045936,0.0003061494,0.000007599257],"category_scores_gemma":[0.00003557201,0.0002264933,0.00009871999,0.0004389372,0.00005827629,0.0002062827,0.001157354,0.0003698434,0.00002951444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001544624,"about_ca_system_score_gemma":0.00008315936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003922824,"about_ca_topic_score_gemma":0.0001326155,"domain_scores_codex":[0.9984397,0.0000596077,0.0001933516,0.0008562629,0.000190478,0.0002606156],"domain_scores_gemma":[0.9978923,0.0002278692,0.0002564809,0.001037558,0.0004992943,0.00008647728],"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":[0.0003083853,0.000447612,0.00005582022,0.000486601,0.000324452,0.000008048803,0.001831694,0.08124622,0.02915256,0.1348833,0.2066472,0.5446081],"study_design_scores_gemma":[0.0003148071,0.0001455164,0.0001321583,0.00006790685,0.00001853173,0.000005789146,0.00003232379,0.811422,0.06010797,0.07211249,0.0551472,0.0004932626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002007747,0.0002004527,0.9919719,0.001097571,0.001050064,0.001661597,0.00001371737,0.0005295324,0.003274377],"genre_scores_gemma":[0.1027719,0.00005889538,0.892695,0.0004504608,0.0007531678,0.001436339,0.0000584017,0.00003282527,0.001743084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7301759,"threshold_uncertainty_score":0.9236127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247477953980892,"score_gpt":0.2995562272536207,"score_spread":0.2770814477138118,"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."}}