Superseded baseline#21 of 80 most-superseded
PD-Quant
PD-Quant: Post-Training Quantization based on Prediction Difference MetricLLM quantization · first seen Dec 14, 2022
superseded — cited as a baseline and beaten by newer methods
0 papers critique it · 3 beat it on benchmarks
Beaten on benchmarks
Head-to-head results where a newer method reports beating PD-Quant. Values are copied from the source paper's tables — verify against the cited paper.
- MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
MGRQ beats PD-Quant · Top-1 accuracy [W4/A4]
70.02 vs 1.51
- MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
MGRQ beats PD-Quant · Top-1 accuracy [W6/A6]
80.39 vs 70.84
- ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
ADFQ-ViT (Ours) beats PD-Quant · Top-1 accuracy on ImageNet [4-bit (W-bit=4, A-bit=4)]
72.14 vs 1.51
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- COD-TDQWhen W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation QuantizationApr 18, 2026
- Joint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data GenerationJoint Post-Training Quantization of Vision Transformers with Learned Prompt-Guided Data GenerationFeb 21, 2026