DTS: dynamic training slimming with feature sparsity for efficient convolutional neural network

Abstract

Deep convolutional neural networks have achieved remarkable progress on computer vision tasks over last years. In this paper, we proposed a dynamic training slimming with feature sparsity based on structured pruning, named DTS, for efficient and automatic channel pruning. Unlike other existing pruning methods, which require manual intervention for pruning settings for each layer, DTS can design suitable architecture width for target datasets and deployment resources by automated pruning. The proposed method can be deployed to modern CNNs and the experimental results on CIFAR, ImageNet and PASCAL VOC benchmark datasets demonstrate the effectiveness of the proposed method, which significantly exceeds the other schemes.