Network pruning using sparse learning and genetic algorithm

Abstract

In recent years, convolutional neural networks (CNNs) have achieved success in the field of computer vision. However, their large storage requirements and high computational consumption limit their scope of application, especially, in mobile devices. Network pruning has been proven to be an effective approach for relieving such burdens. However, the pruning ratio and accuracy of the existing methods need to be improved. In this paper, a scheme of network channel pruning, based on sparse learning and the genetic algorithm, is proposed to achieve a better balance between the pruning ratio and accuracy. A gradient-limited L1/2 regularization is introduced into the model training, to achieve channel-level sparsity. In addition, a dynamically adjustable evaluation factor is proposed to evaluate the importance of channels and the genetic algorithm is utilized to acquire a suitable subnetwork from the sparse model. Our method is evaluated against several state-of-the-art CNNs on three different classification datasets, and is found to achieve, on an average, 63.8%/50.8%/58.6% reduction in parameters and 49.1%/42.9%/58.7% reduction in computational complexity, with negligible loss of accuracy. Our method also achieves 42.84% reduction in parameters and 30.06% reduction in computational complexity of the detection network.