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.

翻译：卷积神经网络（CNNs）是图像分类等计算机视觉任务中应用最广泛的机器学习模型之一。为提升CNNs的运算效率，研究者已开发出多种CNNs压缩方法。其中低秩方法通过将原始卷积核近似分解为一系列更小的卷积核，从而降低存储与时间复杂度。本研究提出一种新颖的低秩CNNs压缩方法，该方法基于缩减存储直接张量环分解（RSDTR）。所提方法具备更高的循环模置换灵活性，在保持压缩网络良好分类精度的同时，实现了参数与浮点运算数（FLOPS）的高压缩比。在CIFAR-10与ImageNet数据集上的实验表明，相较于其他现有先进的CNNs压缩方法，RSDTR展现出显著的高效性。