Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network archite...

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Bibliographic Details
Published in:International journal of computer vision 2022, Vol.130 (1), p.157-178
Main Authors: Zhang, Haokui, Li, Ying, Chen, Hao, Gong, Chengrong, Bai, Zongwen, Shen, Chunhua
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Buildings
Computer architecture
Computer Imaging
Computer Science
Datasets
Deep learning
Design
Image processing
Image Processing and Computer Vision
Image resolution
Image restoration
Methods
Neural networks
Noise
Noise reduction
Pattern Recognition
Pattern Recognition and Graphics
Remodeling, restoration, etc
Search methods
Vision
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Summary:Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network architectures for low-level image restoration tasks. In particular, we propose a memory-efficient hierarchical NAS (termed HiNAS) and apply it to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds a flexible hierarchical search space, including the inner search space and outer search space. They are in charge of designing cell architectures and deciding cell widths, respectively. For the inner search space, we propose a layer-wise architecture sharing strategy, resulting in more flexible architectures and better performance. For the outer search space, we design a cell-sharing strategy to save memory, and considerably accelerate the search speed. The proposed HiNAS method is both memory and computation efficient. With a single GTX1080Ti GPU, it takes only about 1 h for searching for denoising network on the BSD-500 dataset and 3.5 h for searching for the super-resolution structure on the DIV2K dataset. Experiments 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. Code is available at: https://github.com/hkzhang91/HiNAS
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-021-01537-w