Training Quantized Neural Networks With a Full-Precision Auxiliary Module

Bohan Zhuang, Lingqiao Liu, Mingkui Tan, Chunhua Shen, Ian Reid; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1488-1497

Abstract


In this paper, we seek to tackle a challenge in training low-precision networks: the notorious difficulty in propagating gradient through a low-precision network due to the non-differentiable quantization function. We propose a solution by training the low-precision network with a full-precision auxiliary module. Specifically, during training, we construct a mix-precision network by augmenting the original low-precision network with the full precision auxiliary module. Then the augmented mix-precision network and the low-precision network are jointly optimized. This strategy creates additional full-precision routes to update the parameters of the low-precision model, thus making the gradient back-propagates more easily. At the inference time, we discard the auxiliary module without introducing any computational complexity to the low-precision network. We evaluate the proposed method on image classification and object detection over various quantization approaches and show consistent performance increase. In particular, we achieve near lossless performance to the full-precision model by using a 4-bit detector, which is of great practical value.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhuang_2020_CVPR,
author = {Zhuang, Bohan and Liu, Lingqiao and Tan, Mingkui and Shen, Chunhua and Reid, Ian},
title = {Training Quantized Neural Networks With a Full-Precision Auxiliary Module},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}