Structured feature sparsity training for convolutional neural network compression

Abstract

Convolutional neural networks (CNNs) with large model size and computing operations are difficult to be deployed on embedded systems, such as smartphones or AI cameras. In this paper, we propose a novel structured pruning method, termed the structured feature sparsity training (SFST), to speed up the inference process and reduce the memory usage of CNNs. Unlike other existing pruning methods, which require multiple iterations of pruning and retraining to ensure stable performance, SFST only needs to fine-tune the pretrained model with additional regularization on the less important features and then prune them, no multiple pruning and retraining needed. SFST can be deployed to a variety of modern CNN architectures including VGGNet, ResNet and MobileNetv2. Experimental results on CIFAR, SVHN, ImageNet and MSTAR benchmark dataset demonstrate the effectiveness of our scheme, which achieves superior performance over the state-of-the-art methods.