@inproceedings{b6f758a6303344038e29697d71a1a27d,
title = "The Learned Inexact Project Gradient Descent Algorithm",
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.",
keywords = "Algorithm Acceleration, Deep Learning, Inverse Problems, LISTA, Sparse Representation",
author = "Raja Giryes and Eldar, \{Yonina C.\} and Bronstein, \{Alex M.\} and Guillermo Sapiro",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8462136",
language = "English (US)",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6767--6771",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
address = "United States",
}