  Post-training quantization (PTQ) is a neural network compression technique
that converts a full-precision model into a quantized model using
lower-precision data types. Although it can help reduce the size and
computational cost of deep neural networks, it can also introduce quantization
noise and reduce prediction accuracy, especially in extremely low-bit settings.
How to determine the appropriate quantization parameters (e.g., scaling factors
and rounding of weights) is the main problem facing now. Existing methods
attempt to determine these parameters by minimize the distance between features
before and after quantization, but such an approach only considers local
information and may not result in the most optimal quantization parameters. We
analyze this issue and ropose PD-Quant, a method that addresses this limitation
by considering global information. It determines the quantization parameters by
using the information of differences between network prediction before and
after quantization. In addition, PD-Quant can alleviate the overfitting problem
in PTQ caused by the small number of calibration sets by adjusting the
distribution of activations. Experiments show that PD-Quant leads to better
quantization parameters and improves the prediction accuracy of quantized
models, especially in low-bit settings. For example, PD-Quant pushes the
accuracy of ResNet-18 up to 53.14% and RegNetX-600MF up to 40.67% in weight
2-bit activation 2-bit. The code is released at
https://github.com/hustvl/PD-Quant.
