Algorithmic Aspects of Inverse Problems Using Generative Models

Chinmay Hegde

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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 inverse problems, and (b) empirically improves upon conventional techniques such as back-propagation. 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 further building blocks towards an algorithmic theory of generative models in inverse problems.

    Original languageEnglish (US)
    Title of host publication2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages166-172
    Number of pages7
    ISBN (Electronic)9781538665961
    DOIs
    Publication statusPublished - Feb 5 2019
    Event56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018 - Monticello, United States
    Duration: Oct 2 2018Oct 5 2018

    Publication series

    Name2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018

    Conference

    Conference56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018
    CountryUnited States
    CityMonticello
    Period10/2/1810/5/18

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    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Signal Processing
    • Energy Engineering and Power Technology
    • Control and Optimization

    Cite this

    Hegde, C. (2019). Algorithmic Aspects of Inverse Problems Using Generative Models. In 2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018 (pp. 166-172). [8636031] (2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2018.8636031