  Convolutional neural networks (CNNs) are among the most widely used machine
learning models for computer vision tasks, such as image classification. To
improve the efficiency of CNNs, many CNNs compressing approaches have been
developed. Low-rank methods approximate the original convolutional kernel with
a sequence of smaller convolutional kernels, which leads to reduced storage and
time complexities. In this study, we propose a novel low-rank CNNs compression
method that is based on reduced storage direct tensor ring decomposition
(RSDTR). The proposed method offers a higher circular mode permutation
flexibility, and it is characterized by large parameter and FLOPS compression
rates, while preserving a good classification accuracy of the compressed
network. The experiments, performed on the CIFAR-10 and ImageNet datasets,
clearly demonstrate the efficiency of RSDTR in comparison to other
state-of-the-art CNNs compression approaches.
