{"id":"W4415309938","doi":"10.1109/iccv51701.2025.01720","title":"DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization","year":2025,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Quantization (signal processing); Scaling; Outlier; Weighting; Benchmark (surveying); Code (set theory)","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":[0.00005971011,0.00006334875,0.0001040179,0.00004810536,0.00008923415,0.00001342172,0.0000468424,0.00002195688,0.00006127763],"category_scores_gemma":[0.00000661945,0.00005477669,0.00007248276,0.0001005621,0.00001228839,0.00008783098,0.00001784301,0.00004432121,3.984326e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004895575,"about_ca_system_score_gemma":0.00002166899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005228801,"about_ca_topic_score_gemma":0.000002193863,"domain_scores_codex":[0.9995561,0.000009997879,0.0001600041,0.0001207676,0.00004625673,0.0001069038],"domain_scores_gemma":[0.9996928,0.00004699037,0.00005574747,0.00007569086,0.0001068311,0.000021918],"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":[0.00007351497,0.00009986268,0.00295847,0.00003897568,0.00005904825,2.869588e-8,0.001154644,0.06809214,0.01898847,0.7614891,0.0007942464,0.1462515],"study_design_scores_gemma":[0.0004493985,0.00002995891,0.0001817905,0.00005163406,0.00002206826,5.93589e-8,0.003881629,0.963273,0.008489303,0.02323373,0.0002864647,0.000100966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3239711,0.000007164263,0.6635789,0.0001445774,0.0002420498,0.0001400327,0.000004426677,0.00001900081,0.0118928],"genre_scores_gemma":[0.9955778,0.000001274619,0.00196751,0.00005247891,0.00008641606,0.00001328008,0.00003428217,0.000006258084,0.002260721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8951809,"threshold_uncertainty_score":0.2233729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777971651766453,"score_gpt":0.2917487787548991,"score_spread":0.2639690622372346,"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."}}