Abstract
Deep convolutional neural networks (CNNs) with a large number of parameters requires huge computational resources, which has limited the application of CNNs on resources constrained appliances. Decomposition-based methods, therefore, have been utilized to compress CNNs in recent years. However, since the compression factor and performance are negatively correlated, the state-of-the-art works either suffer from severe performance degradation or have limited low compression factors. To overcome these problems, unlike previous works compressing layers separately, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition. The idea is inspired by the fact that there are lots of repeated modules in CNNs, and by projecting weights with the same structures into the same subspace, networks can be further compressed and even accelerated. In particular, three joint matrix decomposition schemes are developed, and the corresponding optimization approaches based on Singular Values Decomposition are proposed. Extensive experiments are conducted across three challenging compact CNNs and 3 benchmark data sets to demonstrate the superior performance of our proposed algorithms. As a result, our methods can compress the size of ResNet-34 by 22x with slighter accuracy degradation compared with several state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2107.04386
