Pu, Wei;
Zhou, Chao;
Eldart, Yonina C;
Rodrigues, Miguel RD;
(2021)
REST: Robust lEarned Shrinkage-Thresholding Network Taming Inverse Problems with Model Mismatch.
In:
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
(pp. pp. 2885-2889).
IEEE: Toronto, ON, Canada.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | REST: Robust lEarned Shrinkage-Thresholding Network Taming Inverse Problems with Model Mismatch |
| Event: | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Location: | ELECTR NETWORK |
| Dates: | 6 Jun 2021 - 11 Jun 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP39728.2021.9414141 |
| Publisher version: | https://doi.org/10.1109/ICASSP39728.2021.9414141 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Acoustics, Computer Science, Artificial Intelligence, Computer Science, Software Engineering, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Computer Science, Engineering, Inverse Problems, Compressive Sensing Problems, Model Mismatch, Robustness, Deep Learning |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150849 |
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