The Learned Inexact Project Gradient Descent Algorithm

Raja Giryes; Yonina C. Eldar; Alexander M. Bronstein; Guillermo Sapiro

ICASSP 2018accepted0 citations

The Learned Inexact Project Gradient Descent Algorithm

Raja Giryes, Yonina C. Eldar, Alexander M. Bronstein, Guillermo Sapiro

Abstract

Accelerating iterative algorithms for solving inverse problems using neural networks have become a very popular strategy in the recent years. In this work, we propose a theoretical analysis that may provide an explanation for its success. Our theory relies on the usage of inexact projections with the projected gradient descent (PGD) method. It is demonstrated in various problems including image super-resolution.

BibTeX

@inproceedings{icassp2018_thelearnedinexac,
  title = {The Learned Inexact Project Gradient Descent Algorithm},
  author = {Raja Giryes and Yonina C. Eldar and Alexander M. Bronstein and Guillermo Sapiro},
  booktitle = {ICASSP 2018},
  year = {2018}
}