{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099389,0.001030612,0.0007088779,0.0005245339,0.0004349725,0.0008606525,0.001741713,0.0008695484,0.002777646],"category_scores_gemma":[0.006289151,0.0004195213,0.0005720668,0.0005469277,0.0007264028,0.001539167,0.001557751,0.002303318,0.0007540981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007136563,"about_ca_system_score_gemma":0.001149563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004048401,"about_ca_topic_score_gemma":0.006297038,"domain_scores_codex":[0.999466,0.000100136,0.00003385829,0.0001220451,0.0002189431,0.00005904743],"domain_scores_gemma":[0.998574,0.0005502881,0.000147817,0.0003045027,0.000354354,0.00006896756],"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":[0.0002298831,0.000117608,0.002042622,0.0001552771,0.00006200943,0.0001591039,0.0002297012,0.4330878,0.03530454,0.01705952,0.006961866,0.5045902],"study_design_scores_gemma":[0.000007164928,0.00002028233,0.0001360786,0.000005861305,0.000004303348,0.00002485155,0.00001119384,0.9899817,0.005815125,0.003127561,0.0008592856,0.000006508088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01483136,0.0001574153,0.9822022,0.000157824,0.00005534416,0.00004035572,0.00005711017,0.001853548,0.0006448055],"genre_scores_gemma":[0.4296572,0.0002798249,0.5637957,0.0003216185,0.00006344065,0.0002047469,0.0005479803,0.000828245,0.004301301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004048401,"threshold_uncertainty_score":0.009292126,"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."}}