Paper Info
Title
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Abstract
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.
Year
Venue
Keywords
2019
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
special operations
Field
DocType
Volume
Convolutional neural network,Computer science,Decorator pattern,Artificial intelligence,Machine learning,Pruning
Conference
32
ISSN
Citations 
PageRank 
1049-5258
2
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
You, Zhonghui120.36
Yan, Kun221.37
Jinmian Ye3212.41
Meng Ma48212.29
Ping Wang514914.37