Convolutional Neural Network Compression Method Based on Multi-Factor Channel Pruning

Deep neural networks have been widely applied across various domains, but their numerous parameters and high computational demands limit their practical usage scenarios. To address this issue, this paper introduces a convolutional neural network compression method based on multi-factor channel pruning. By integrating scaling and shifting factors from batch normalization layers, a multi-factor channel salience metric is proposed to measure channel importance. By removing redundant channels within the convolutional neural network, a compressed model is obtained. On the CIFAR-10 dataset, we pruned 93.06% of the parameters and 91.92% of the calculations from the VGG13BN network, with only a 2.81% decrease in accuracy. On the CIFAR-100 dataset, we pruned 72.84% of the parameters and 72.03% of the calculations from the VGG13BN network, with an accuracy improvement of 4.11%.