Robust Deep Learning Models for Imaging Inverse Problems Modern imaging problems increasingly rely on deep generative models, including score-based and flow-matching models, as well as neural networks used as implicit priors. Although these methods can produce high-quality reconstructions, their reliability may deteriorate when deployment conditions differ from those encountered during training. Such changes may affect the image distribution, the measurement process, or other characteristics of the imaging problem. This work investigates the impact of these mismatches on reconstruction algori…
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