aiqbench analysis
The paper argues that deep unrolling, which pairs classical iterative solvers with learned components, can stay robust when the measurement operator's geometry shifts between training and deployment. It proposes a phase retrieval method that adds a learned denoising step to alternating projections, and reports good reconstructions without retraining when the operator's dynamic range changes at test time. For ML readers, the takeaway is that algorithm choice, not just the learned prior, may determine how well learned reconstruction generalizes under distribution shift, though the evidence is limited to the settings the abstract describes.
Deeply unrolled alternating projections for phase retrieval
arXiv:2610.17316v1 Announce Type: cross Abstract: Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These methods have been developed under the implicit assumption that the geometry of the measurement operator does not change between training and deployment; when this is no longer the case, the quality of obtained reconstructions can degrade significantly. In this paper, we argue that this is not an inherent limitation of the deep unrolling paradigm and can be mitigated by a careful choice of the underlying iterative algorithm. We propose a deep unrolling framework for the phase retrieval problem that augments the alternating projections method with a learned denoising step. Our method obtains high-quality reconstructions without the need for retraining in settings where conventional model-based alternatives struggle, such as when the dynamic range of the measurement operator changes significantly at test time.
Abstract:Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These methods have been developed under the implicit assumption that the geometry of the measurement operator does not change between training and deployment; when this is no longer the case, the quality of obtained reconstructions can degrade significantly. In this paper, we argue that this is not an inherent limitation of the deep unrolling paradigm and can be mitigated by a careful choice of the underlying iterative algorithm. We propose a deep unrolling framework for the phase retrieval problem that augments the alternating projections method with a learned denoising step. Our method obtains high-quality reconstructions without the need for retraining in settings where conventional model-based alternatives struggle, such as when the dynamic range of the measurement operator changes significantly at test time.
Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2610.17316 [stat.ML]
(or arXiv:2610.17316v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.17316 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 239 words
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