Hierarchical Neural Architecture Search for Single Image Super-Resolution

By Yong Guo, Yongsheng Luo, Zhenhao He, Jin Huang, Jian Chen. 2020

53 citations recorded by the source corpus.

Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

Subfields: Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Mingkui Tan
  2. Jian Chen
  3. Yong Guo
  4. Qi Chen
  5. Jingdong Wang
  6. Jiezhang Cao
  7. Yanwu Xu
  8. Zeshuai Deng

In Neural Architecture Search & AutoML

  1. Yong Guo
  2. Jingdong Wang
  3. Jian Chen

Citations in the corpus