Dynamic and Adaptive Threshold for DNN Compression from Scratch

Abstract

Despite their great success, deep neural networks (DNN) are hard to deploy on devices with limited hardware like mobile phones because of massive parameters. Many methods have been proposed for DNN compression, i.e., to reduce the parameters of DNN models. However, almost all of them are based on reference models, which were firstly trained. In this paper, we propose an approach to perform DNN training and compression simultaneously. More concretely, a dynamic and adaptive threshold (DAT) framework is utilized to prune a DNN gradually by changing the pruning threshold during training. Experiments show that DAT can not only reach comparable or better compression rate almost without loss of accuracy than state-of-the-art DNN compression methods, but also beat DNN sparse training methods by a large margin.