  Low-bit quantization is challenging to maintain high performance with limited
model capacity (e.g., 4-bit for both weights and activations). Naturally, the
distribution of both weights and activations in deep neural network are
Gaussian-like. Nevertheless, due to the limited bitwidth of low-bit model,
uniform-like distributed weights and activations have been proved to be more
friendly to quantization while preserving accuracy~\cite{Han2015Learning}.
Motivated by this, we propose Scale-Clip, a Distribution Reshaping technique
that can reshape weights or activations into a uniform-like distribution in a
dynamic manner. Furthermore, to increase the model capability for a low-bit
model, a novel Group-based Quantization algorithm is proposed to split the
filters into several groups. Different groups can learn different quantization
parameters, which can be elegantly merged in to batch normalization layer
without extra computational cost in the inference stage. Finally, we integrate
Scale-Clip technique with Group-based Quantization algorithm and propose the
Group-based Distribution Reshaping Quantization (GDQR) framework to further
improve the quantization performance. Experiments on various networks (e.g.
VGGNet and ResNet) and vision tasks (e.g. classification, detection and
segmentation) demonstrate that our framework achieves good performance.
