INVERSE IMAGING WITH GENERATIVE PRIORS VIA LANGEVIN DYNAMICS

Thanh V. Nguyen, Gauri Jagatap, Chinmay Hegde

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

    Abstract

    Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. Here, we assume an unknown signal to lie in the range of some pre-trained generative model. A popular approach for signal recovery is via gradient descent in the low-dimensional latent space. While gradient descent has achieved good empirical performance, its theoretical behavior is not well understood. In this paper, we introduce the use of stochastic gradient Langevin dynamics (SGLD) for compressed sensing with a generative prior. Under mild assumptions on the generative model, we prove the convergence of SGLD to the true signal. We also demonstrate competitive empirical performance to standard gradient descent.

    Original languageEnglish (US)
    Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages8672-8676
    Number of pages5
    ISBN (Electronic)9781665405409
    DOIs
    StatePublished - 2022
    Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
    Duration: May 23 2022May 27 2022

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Volume2022-May
    ISSN (Print)1520-6149

    Conference

    Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
    Country/TerritorySingapore
    CityVirtual, Online
    Period5/23/225/27/22

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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