Channel Pruning for Accelerating Very Deep Neural Networks [article]

Yihui He, Xiangyu Zhang, Jian Sun
2017 arXiv   pre-print
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
more » ... tate-of-the-art results by 5x 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 2x speed-up respectively, which is significant. Code has been made publicly available.
arXiv:1707.06168v2 fatcat:tswkbanmtzdbjcnk2hh3m642ci