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Two dimensional Convolutional Neural Networks (Con-vNets) have been widely adopted as powerful models and have achieved state-of-the-art performance in many image related tasks. However, their extensions are still struggling for leading performance for high dimensional (HD) signal processing, partially due to the explosion of training parameters, greatly enhanced computational complexity and memory cost. In this paper, we present a simple, lightweight, yet efficient ConvNet, called S-Net, for the HD signal processing by allowing a separable structure on the ConvNet throughout the learning process. It takes advantage of a series of one dimensional convolution kernels to handle N-dimensional (ND) signals. Thus, the presented S-Net significantly reduces the training complexity, parameters, and memory cost. The proposed S-Net is evaluated on both 2D and 3D benchmarks C Γ A R-10 and KTH. Experimental results show that the S-Net achieves competitive performance with greatly reduced computational and memory costs in comparison with the state-of-the-art ConvNet models. © 2018 IEEE.
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