Constant-Expansion Suffices for Compressed Sensing with Generative Priors

NIPS 2020, 2020.

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In Appendix C, we provide a detailed explanation of why the Weight Distribution Condition arises and we give a sketch of the basic theory of global landscape analysis for compressed sensing with generative priors

Abstract:

Generative neural networks have been empirically found very promising in providing effective structural priors for compressed sensing, since they can be trained to span low-dimensional data manifolds in high-dimensional signal spaces. Despite the non-convexity of the resulting optimization problem, it has also been shown theoretically t...More

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