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We propose GAN-Flow -- a modular inference approach that combines generative adversarial network (GAN) prior with a normalizing flow (NF) model to solve inverse problems in the lower-dimensional latent space of the GAN prior using variational inference. GAN-Flow leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs, and the capability of NFs to efficiently approximate complicated posterior distributions. In this work, we apply GAN-Flow to solve two physics-based linear inverse problems. Results show that GAN-Flow can efficiently approximate the posterior distribution in such high-dimensional problems.
Author Information
Agnimitra Dasgupta (University of Southern California)
I am a PhD candidate and Provost Fellow at the Sonny Astani Department of Civil & Environmental Engineering, University of Southern California. My research interests lie at the intersection of uncertainty quantification, statistical learning and scientific machine learning with applications in problems ranging from imaging to mechanics.
Dhruv Patel (Stanford University)
Deep Ray (University of Southern California)
Erik Johnson (University of Southern California)
Assad Oberai (University of Southern California)
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