Degree Type

Thesis

Date of Award

2019

Degree Name

Master of Science

Department

Electrical and Computer Engineering

Major

Electrical Engineering

First Advisor

Chinmay . Hegde

Abstract

The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by generative adversarial networks, or GANs). In this work, we study the algorithmic aspects of such a learning-based approach from a theoretical perspective. For certain generative network architectures, we establish a simple non-convex algorithmic approach that (a) theoretically enjoys linear convergence guarantees for certain linear and nonlinear inverse problems, and (b) empirically improves upon conventional techniques such as back-propagation. We support our claims with the experimental results for solving various inverse problems. We also propose an extension of our approach that can handle model mismatch (i.e., situations where the generative network prior is not exactly applicable.) Together, our contributions serve as building blocks towards a principled use of generative models in inverse problems with more complete algorithmic understanding.

Copyright Owner

Viraj Shah

Language

en

File Format

application/pdf

File Size

34 pages

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