Wang, X;
Benning, M;
Repetti, A;
(2024)
A Lifted Bregman Strategy for Training Unfolded Proximal Neural Network Gaussian Denoisers.
In:
Proceedings of the 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP).
IEEE: London, UK.
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Abstract
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a specific task by unrolling a proximal algorithm for a fixed number of iterations, where linearities can be learned from prior training procedure. PNNs have shown to be more robust than traditional deep learning approaches while reaching at least as good performances, in particular in computational imaging. However, training PNNs still depends on the efficiency of available training algorithms. In this work, we propose a lifted training formulation based on Bregman distances for unfolded PNNs. Leveraging the deterministic mini-batch block-coordinate forward-backward method, we design a bespoke computational strategy beyond traditional back-propagation methods for solving the resulting learning problem efficiently. We assess the behaviour of the proposed training approach for PNNs through numerical simulations on image denoising, considering a denoising PNN whose structure is based on dual proximal-gradient iterations.
| Type: | Proceedings paper |
|---|---|
| Title: | A Lifted Bregman Strategy for Training Unfolded Proximal Neural Network Gaussian Denoisers |
| Event: | 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) |
| Dates: | 22 Sep 2024 - 25 Sep 2024 |
| ISBN-13: | 979-8-3503-7225-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MLSP58920.2024.10734766 |
| Publisher version: | https://doi.org/10.1109/MLSP58920.2024.10734766 |
| 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: | Image Gaussian denoising, unfolding, proximal neural networks, Bregman distance |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201424 |
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