Channel Pruning Base on Joint Reconstruction Error for Neural Network

In this paper, we propose a neural network channel pruning method based on the joint reconstruction error (JRE). To preserve the global discrimination ability of a pruned neural network, we propose the global reconstruction error. To ensure the integrity of information in the forward propagation process of a neural network, we propose the local reconstruction error. Finally, through normalization, the two magnitude mismatched losses are combined to obtain the joint error. The baseline network and pruned network are symmetrical structures. The importance of each channel in the pruned network is determined by the joint error between the channel and the corresponding channel in the baseline network. The proposed method prunes the channels in the pruned network according to the importance score and then restores its accuracy. The proposed method reduces the scale of the neural network and speeds up the model inferring speed without losing the accuracy of the neural network. Experimental results show the effectiveness of the method. For example, on the CIFAR-10 dataset, the proposed method prunes 50% of the channels of the VGG16 model, and the accuracy of the pruned model is 0.46% higher than that of the original model.