Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

• URL: http://arxiv.org/abs/2012.13212v2
• Date: Mon, 28 Dec 2020 03:28:13 GMT
• Title: Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
• Authors: Haokui Zhang, Ying Li, Chengrong Gong, Hao Chen, Zongwen Bai, Chunhua Shen
• Abstract summary: We propose a memory-efficient hierarchical NAS HiNAS (HiNAS) for image denoising and image super-resolution tasks. With a single GTX1080Ti GPU, it takes only about 1 hour for searching for denoising network on BSD 500 and 3.5 hours for searching for the super-resolution structure on DIV2K.
• Score: 68.6505473346005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, much attention has been spent on neural architecture search (NAS) approaches, which often outperform manually designed architectures on highlevel vision tasks. Inspired by this, we attempt to leverage NAS technique to automatically design efficient network architectures for low-level image restoration tasks. In this paper, we propose a memory-efficient hierarchical NAS HiNAS (HiNAS) and apply to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds an flexible hierarchical search space, including inner search space and outer search space, which in charge of designing cell architectures and deciding cell widths, respectively. For inner search space, we propose layerwise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance. For outer search space, we propose cell sharing strategy to save memory, and considerably accelerate the search speed. The proposed HiNAS is both memory and computation efficient. With a single GTX1080Ti GPU, it takes only about 1 hour for searching for denoising network on BSD 500 and 3.5 hours for searching for the super-resolution structure on DIV2K. Experimental results show that the architectures found by HiNAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods.

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