An Efficient Channel-level Pruning for CNNs without Fine-tuning

Abstract

The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Model compression techniques are able to remove a significant fraction of network parameters to reduce the costs. Channel pruning is among the predominant approaches to compress networks. The typical framework of existing channel pruning methods consists of three steps: training a dense network, pruning redundant parameters, and fine-tuning to increase model accuracy. However, the fine-tuning step usually takes a long time, which is even close to the training costs. Besides, the decoupling of training and pruning leads to that many unimportant parameters that will be removed later still need to be trained. To address these issues, we propose the Dynamic Mask-based Channel Pruning (DMCP) method in this study. The algorithm zeros out unimportant channels with mask vectors and prunes the redundant weights during training. It achieves comparable model accuracy with existing methods, without the fine-tuning step. Moreover, DMCP prunes parameters and reconfigures models during training, so that the number of operations for training useless parameters is reduced. In our evaluations, DMCP removes up to 82% parameters for VGG16, VGG19, and ResNet18 on CIFAR10 dataset, and it reduces up to 58% floating point operation (FLOPs) of training.