A Novel Channel Pruning Approach based on Local Attention and Global Ranking for CNN Model Compression
Wei Lu, Yang Jiang, Peiguang Jing, Jinghui Chu, Fugui Fan · 2023
Channel pruning facilitates the acceleration and deployment of convolutional neural networks on resource-constrained devices. Nevertheless, existing related methods mainly focus on the importance of an individual channel, neglecting the intra-layer relationship and inter-layer influence. In this paper, we propose a novel local attention and global ranking (LAGR) method for channel pruning. Specifically, we first introduce the attention mechanism to explore the local correlation between channels of the intra-layer. On this basis, we evaluate the global ranking of all channels across the network by the normalization operation. Besides, we introduce a noisy training strategy in the pre-training stage to ensure a balanced weight distribution. Extensive experiments conducted on three representative networks, including VGGNet, GoogLeNet, and ResNet, have demonstrated the superior performance of the proposed method in comparison with several state-of-the-art methods.