Reduced storage direct tensor ring decomposition for convolutional neural networks compression
This work addresses the need for more efficient CNNs in computer vision, offering a domain-specific improvement for model compression.
The paper tackles the problem of compressing convolutional neural networks (CNNs) for computer vision tasks by proposing a novel low-rank compression method based on reduced storage direct tensor ring decomposition (RSDTR), which achieves high parameter and FLOPS compression rates while maintaining good classification accuracy on CIFAR-10 and ImageNet datasets.
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.