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 compression approaches have been developed. Low-rank methods approximate the original convolutional kernel with a sequence of smaller convolutional kernels, leading to reduced storage and time complexities. In this study, we propose a novel low-rank CNN 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 CNN compression approaches.
Gabor et al. (Fri,) studied this question.