Channel Pruning for Accelerating Very Deep Neural Networks

作者：Yihui He, Xiangyu Zhang, Jian Sun · 年份：2017 · DOI：10.1109/iccv.2017.155 · 被引用次数：2584 · 研究领域：Advanced Neural Network Applications、Adversarial Robustness in Machine Learning、Domain Adaptation and Few-Shot Learning

In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5× speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2× speedup respectively, which is significant.