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Breaking Symmetries in Data-Driven Phase Retrieval

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Abstract

The existing iterative and data-driven methods fail to solve phase retrieval due to the intrinsic problem symmetries. We propose two end-to-end learning methods that break the barrier and work in a new regime.

© 2021 The Author(s)

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