  With the development of deep neural networks, the size of network models
becomes larger and larger. Model compression has become an urgent need for
deploying these network models to mobile or embedded devices. Model
quantization is a representative model compression technique. Although a lot of
quantization methods have been proposed, many of them suffer from a high
quantization error caused by a long-tail distribution of network weights. In
this paper, we propose a novel quantization method, called weight normalization
based quantization (WNQ), for model compression. WNQ adopts weight
normalization to avoid the long-tail distribution of network weights and
subsequently reduces the quantization error. Experiments on CIFAR-100 and
ImageNet show that WNQ can outperform other baselines to achieve
state-of-the-art performance.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning