{"id":"W3162473050","doi":"10.1109/ojcas.2021.3072884","title":"Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence","year":2021,"lang":"en","type":"article","venue":"IEEE Open Journal of Circuits and Systems","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Edge device; Codec; Quantization (signal processing); Clipping (morphology); Artificial neural network; Entropy encoding; Algorithm; Floating point; Data compression; Cloud computing; Computer engineering; Artificial intelligence; Computer hardware","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":[0.0004203876,0.0005917574,0.0003047952,0.0004433613,0.0002217217,0.0005623279,0.000833675,0.0004309766,0.001996026],"category_scores_gemma":[0.002251672,0.0001530315,0.000241113,0.0005017034,0.0003967015,0.00126409,0.0007648413,0.001072465,0.0004800929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005791912,"about_ca_system_score_gemma":0.0004524495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001875486,"about_ca_topic_score_gemma":0.00383839,"domain_scores_codex":[0.9997137,0.00002701594,0.00001882725,0.00004211342,0.0001706711,0.00002759285],"domain_scores_gemma":[0.9993405,0.0002237361,0.000060381,0.0001861914,0.0001677668,0.00002150257],"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":[0.0003807005,0.0001252947,0.001329254,0.0001475942,0.0000431357,0.0003053488,0.0001775677,0.1634081,0.1261894,0.02175144,0.004703034,0.6814391],"study_design_scores_gemma":[0.0000161358,0.0001231166,0.0008198805,0.00002448889,0.00001463192,0.0001405807,0.00003170134,0.9074827,0.07721538,0.008797896,0.005315428,0.00001805461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04005131,0.0002980751,0.9559783,0.0001731941,0.00008480553,0.00005888205,0.0001677879,0.001060039,0.002127623],"genre_scores_gemma":[0.629968,0.0003076146,0.364502,0.0002022832,0.00006964987,0.0001183247,0.0005519385,0.0001621958,0.004118187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001996026,"threshold_uncertainty_score":0.006677449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04553503080435983,"score_gpt":0.3283867181272734,"score_spread":0.2828516873229136,"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."}}