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Generative Priors in Phase Retrieval by veli is a document available to read on EtoBox.

This document addresses the challenge of compressive phase retrieval, demonstrating that deep generative priors can achieve optimal sample complexity with a tractable algorithm, overcoming limitations faced by sparsity priors. The authors provide both theoretical and empirical evidence supporting the use of generative models in signal recovery, highlighting their advantages in representing natural signals and optimizing over the signal manifold. The findings suggest that generative priors represent a signif

Author

veli

Language

EN