Related papers: Theoretical Perspectives on Deep Learning Methods in Inverse Problems

Theoretical Perspectives on Deep Learning Methods in Inverse Problems

URL: http://arxiv.org/abs/2206.14373v1
Date: Wed, 29 Jun 2022 02:37:50 GMT
Title: Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Authors: Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues, Paul Hand, and Yonina C. Eldar
Abstract summary: We focus on generative priors, untrained neural network priors, and unfolding algorithms. In addition to summarizing existing results in these topics, we highlight several ongoing challenges and open problems.
Score: 115.93934028666845
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resolution. While this line of works has predominantly been driven by practical algorithms and experiments, it has also given rise to a variety of intriguing theoretical problems. In this paper, we survey some of the prominent theoretical developments in this line of works, focusing in particular on generative priors, untrained neural network priors, and unfolding algorithms. In addition to summarizing existing results in these topics, we highlight several ongoing challenges and open problems.

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