Slim-RFFNet: Slim deep convolution random Fourier feature network for image classification

Abstract

The standard kernel method is computationally expensive because it needs to store and compute the inverse of the Gram matrix. Furthermore, the classification accuracy of the single kernel method is not effective or efficient as the method’s ability to extract features. The random Fourier feature method establishes a connection between the kernel method and deep learning to solve the above problems. In this paper, we propose a novel slim deep random Fourier feature network named Slim-RFFNet, which introduces convolution into kernel learning. We use the hierarchical strategy and skip connection to construct a deep network structure and lighten the model by using quantization. Experiments conducted on classification benchmarks MNIST and CIFAR10 demonstrate that the proposed Slim-RFFNet significantly outperforms current state-of-the-art deep kernel learning methods. Our algorithm also achieves a trade-off between accuracy and latency. The proposed network can be applied to resource-constrained embedded AI devices. The experimental results on the edge computing system show that our algorithm has a small memory footprint and fast inference speed on small edge devices, and thus meets the requirements for practical applications.