Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
This work addresses the need for efficient deep learning models in resource-constrained applications, though it is incremental as it builds on existing filter pruning techniques.
The paper tackles the problem of accelerating and compressing convolutional neural networks by proposing a global filter pruning method called Gate Decorator, which uses Taylor expansion to rank filter importance and prunes unimportant filters, achieving a 70% FLOPs reduction on ResNet-56 without accuracy loss and a 40% FLOPs reduction on ResNet-50 with a 0.31% top-1 accuracy improvement.
Filter pruning is one of the most effective ways to accelerate and compress convolutional neural networks (CNNs). In this work, we propose a global filter pruning algorithm called Gate Decorator, which transforms a vanilla CNN module by multiplying its output by the channel-wise scaling factors, i.e. gate. When the scaling factor is set to zero, it is equivalent to removing the corresponding filter. We use Taylor expansion to estimate the change in the loss function caused by setting the scaling factor to zero and use the estimation for the global filter importance ranking. Then we prune the network by removing those unimportant filters. After pruning, we merge all the scaling factors into its original module, so no special operations or structures are introduced. Moreover, we propose an iterative pruning framework called Tick-Tock to improve pruning accuracy. The extensive experiments demonstrate the effectiveness of our approaches. For example, we achieve the state-of-the-art pruning ratio on ResNet-56 by reducing 70% FLOPs without noticeable loss in accuracy. For ResNet-50 on ImageNet, our pruned model with 40% FLOPs reduction outperforms the baseline model by 0.31% in top-1 accuracy. Various datasets are used, including CIFAR-10, CIFAR-100, CUB-200, ImageNet ILSVRC-12 and PASCAL VOC 2011. Code is available at github.com/youzhonghui/gate-decorator-pruning