{"id":"W4413146431","doi":"10.1109/cvpr52734.2025.00715","title":"Multirate Neural Image Compression with Adaptive Lattice Vector Quantization","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Vector quantization; Image compression; Computer science; Learning vector quantization; Data compression; Quantization (signal processing); Lattice (music); Artificial neural network; Artificial intelligence; Computer vision; Algorithm; Image (mathematics); Image processing; Physics; Acoustics","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":[0.0006750861,0.0004070125,0.0005092752,0.0005394783,0.0001605702,0.0005325518,0.0009808774,0.000527302,0.0009484803],"category_scores_gemma":[0.001739777,0.0002001205,0.0002989689,0.0006949652,0.0005303493,0.001336697,0.0007462605,0.0009361428,0.000281339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00057495,"about_ca_system_score_gemma":0.0004952443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002587878,"about_ca_topic_score_gemma":0.00342581,"domain_scores_codex":[0.9996612,0.00007315051,0.00002080068,0.00005112478,0.0001683523,0.00002524942],"domain_scores_gemma":[0.9995402,0.0001875221,0.00005711612,0.00007715858,0.0001172794,0.00002084086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556994,0.0001147151,0.0005890429,0.0001278677,0.00003997912,0.00007514796,0.00008022669,0.5144832,0.02465694,0.02766722,0.001979188,0.4300308],"study_design_scores_gemma":[0.000005233613,0.00001679845,0.00004104654,0.000004031415,0.000001892226,0.00001600881,0.000002904966,0.995159,0.002511847,0.001890571,0.000346312,0.000004385008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01330941,0.0004792318,0.9842409,0.0001273078,0.00004222627,0.00002721989,0.00003041108,0.000423642,0.001319587],"genre_scores_gemma":[0.4620173,0.0006851384,0.5333994,0.0001988671,0.00008683783,0.000113365,0.0001515111,0.0000966888,0.003250916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002587878,"threshold_uncertainty_score":0.005145609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150546935772587,"score_gpt":0.2893300090282168,"score_spread":0.2742753154509581,"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."}}