@inproceedings{3e24531cfb0e450abcfead60e50c4eb6,
title = "REST: Robust lEarned Shrinkage: Thresholding Network Taming Inverse Problems with Model Mismatch",
abstract = "We consider compressive sensing problems with model mismatch where one wishes to recover a sparse high-dimensional vector from low-dimensional observations subject to uncertainty in the measurement operator. In particular, we design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. Our proposed network -named Robust lErned Shrinkage-Thresholding (REST) -exhibits additional features including enlarged number of parameters and normalization processing compared to state-of-the-art deep architecture Learned Iterative Shrinkage-Thresholding Algorithm (LISTA), leading to the reliable recovery of the signal under sample-wise varying model mismatch. Our proposed network is also shown to outperform LISTA in compressive sensing problems under sample-wise varying model mismatch.",
author = "Wei Pu and Chao Zhou and Eldar, {Yonina C} and Rodrigues, {Miguel R. D}",
year = "2021",
month = may,
day = "13",
doi = "https://doi.org/10.1109/ICASSP39728.2021.9414141",
language = "الإنجليزيّة",
isbn = "978-1-7281-7606-2",
series = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
pages = "2885--2889",
booktitle = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
note = "2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ; Conference date: 06-06-2021 Through 11-06-2021",
}