Quantifying uncertainty with GAN-based priors

Dhruv V. Patel, Assad A. Oberai

TL;DR: Quantifying uncertainty in inference via GAN priors

Abstract: Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to characterize mathematically. In this work we demonstrate how the approximate distribution learned by a generative adversarial network (GAN) may be used as a prior in a Bayesian update to address both these challenges. We demonstrate the efficacy of this approach by inferring and quantifying uncertainty in inference problems arising in computer vision and physics-based applications. In both instances we highlight the role of computing uncertainty in providing a measure of confidence in the solution, and in designing successive measurements to improve this confidence.

Code: https://github.com/iclr-2020/BI-GANP

Keywords: Bayesian inference, Uncertainty quantification, Generative adversarial networks