{"id":"W4386597007","doi":"10.1109/icip49359.2023.10221982","title":"JPEG Compliant Compression for DNN Vision","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"JPEG; Computer science; Image compression; Artificial intelligence; Quantization (signal processing); Pixel; Lossless JPEG; JPEG 2000; Data compression; Data compression ratio; Artificial neural network; Compression ratio; Computer vision; Deep neural networks; Image processing; Image (mathematics); Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007869826,0.00007305982,0.00008212214,0.00005333873,0.0001989321,0.00004727457,0.0005498735,0.00002495811,0.00001000709],"category_scores_gemma":[0.0000110754,0.00005862715,0.00004016537,0.0005155361,0.00001842983,0.0002226035,0.0002898572,0.00004518016,0.0003952107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001401029,"about_ca_system_score_gemma":0.000008552061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001375022,"about_ca_topic_score_gemma":0.000002399847,"domain_scores_codex":[0.9992296,0.00001156858,0.000128504,0.0002942209,0.0001220453,0.0002140884],"domain_scores_gemma":[0.9991356,0.0002657155,0.00003803805,0.000453098,0.00004302966,0.00006447989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008177325,0.00005830801,0.00005720963,0.00001619417,0.000004934882,0.000003197272,0.00008082057,0.008674835,0.024648,0.4529514,0.3125712,0.2009257],"study_design_scores_gemma":[0.0001992909,0.00004382015,0.001280741,0.00001228351,0.00000102699,0.000002468227,0.000005445607,0.7927318,0.003213669,0.03662095,0.1657822,0.0001063986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001311334,0.00001454999,0.991014,0.004445631,0.0001780143,0.000346856,0.000002503073,0.0008667731,0.001820321],"genre_scores_gemma":[0.4723144,0.00004284999,0.5186657,0.001477206,0.0001790049,0.0003360707,0.00005075457,0.00002620362,0.00690779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7840569,"threshold_uncertainty_score":0.5079766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04797949755490941,"score_gpt":0.3473820569376926,"score_spread":0.2994025593827833,"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."}}