Deep Learning and Neural Architecture Search for Optimizing Binary Neural Network Image Super Resolution

Author:

Su Yuanxin12,Ang Li-minn3,Seng Kah Phooi13,Smith Jeremy2

Affiliation:

1. XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong Liverpool University, Taicang 215400, China

2. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK

3. School of Science, Technology and Engineering, University of the Sunshine Coast, Moreton Bay, QLD 4502, Australia

Abstract

The evolution of super-resolution (SR) technology has seen significant advancements through the adoption of deep learning methods. However, the deployment of such models by resource-constrained devices necessitates models that not only perform efficiently, but also conserve computational resources. Binary neural networks (BNNs) offer a promising solution by minimizing the data precision to binary levels, thus reducing the computational complexity and memory requirements. However, for BNNs, an effective architecture is essential due to their inherent limitations in representing information. Designing such architectures traditionally requires extensive computational resources and time. With the advancement in neural architecture search (NAS), differentiable NAS has emerged as an attractive solution for efficiently crafting network structures. In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. We adapt the search space specifically for super resolution to ensure it is optimally suited for the requirements of such tasks. Furthermore, we incorporate Libra Parameter Binarization (Libra-PB) to maximize information retention during forward propagation. Our experimental results demonstrate that the network structures generated by our method require only a third of the parameters, compared to conventional methods, and yet deliver comparable performance.

Publisher

MDPI AG

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