GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems

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.