  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 (LWAS), 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 hour for searching for denoising
network on the BSD-500 dataset and 3.5 hours 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
