For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial learning difficulties.
Mar 18, 2024
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Mar 18, 2024 · To temper the difficulty, we propose a novel technique to preprocess the training set before training, which we call symmetry breaking. We show ...
We highlight a fundamental difficulty for learning that previous work has ne- glected, likely due to the biased datasets they use for training and evaluation.
We highlight a fundamental difficulty for learning that previous work has ne- glected, likely due to the biased datasets they use for training and evaluation.
It explains how intrinsic symmetries can lead to learning difficulties and proposes a novel technique to preprocess training sets before learning. The paper ...
Symmetry breaking for learning square root To see why symmetries can cause learning difficulties, consider a simple IP: given ; The culprit is the intrinsic sign ...
This work considers the end-to-end deep learning approach for phase retrieval and proposes a simple yet different formulation for PR that seems to overcome ...
For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches ...
We highlight a fundamental difficulty for learning that previous work has neglected, likely due to the biased datasets they use for training and evaluation. We ...
Aug 17, 2022 · Phase retrieval aims to reconstruct a complex-valued signal from its intensity-only measurements. It is a crucial problem in crystallography, ...