  Deep convolutional neural networks (CNNs) with a large number of parameters
require intensive computational resources, and thus are hard to be deployed in
resource-constrained platforms. 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 relatively low
compression factors. To overcome this problem, we propose to compress CNNs and
alleviate performance degradation via joint matrix decomposition, which is
different from existing works that compressed layers separately. The idea is
inspired by the fact that there are lots of repeated modules in CNNs. By
projecting weights with the same structures into the same subspace, networks
can be jointly compressed with larger ranks. In particular, three joint matrix
decomposition schemes are developed, and the corresponding optimization
approaches based on Singular Value Decomposition are proposed. Extensive
experiments are conducted across three challenging compact CNNs for different
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.
