Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks-Reference-Cited by-同舟云学术

Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks

Author:

Zheng Ziyang¹,Dai Wenrui²,Xue Duoduo¹,Li Chenglin¹,Zou Junni³,Xiong Hongkai⁴

Affiliation:

1. Department of Electronic Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China

2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China, 200240

3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China

4. Electronic Engineering, Shanghai Jiao Tong University, 12474 Shanghai, Shanghai, China

Funder

National Natural Science Foundation of China

Program of Shanghai Science and Technology Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference61 articles.

1. Adam: A method for stochastic optimization;kingma;Proc Int Conf Learn Rep,2015

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