• Corpus ID: 204877760

# Lower Bounds for Compressed Sensing with Generative Models

@article{Kamath2019LowerBF,
title={Lower Bounds for Compressed Sensing with Generative Models},
author={Akshay Kamath and Sushrut Karmalkar and Eric Price},
journal={ArXiv},
year={2019},
volume={abs/1912.02938}
}
• Published 14 September 2019
• Computer Science
• ArXiv
The goal of compressed sensing is to learn a structured signal $x$ from a limited number of noisy linear measurements $y \approx Ax$. In traditional compressed sensing, "structure" is represented by sparsity in some known basis. Inspired by the success of deep learning in modeling images, recent work starting with~\cite{BJPD17} has instead considered structure to come from a generative model $G: \mathbb{R}^k \to \mathbb{R}^n$. We present two results establishing the difficulty of this latter…
16 Citations

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