Faster Guaranteed GAN-based recovery in Linear Inverse Problems

Wednesday, October 16, 2019 - 9:50am - 10:35am
Keller 3-180
Yoram Bresler (University of Illinois at Urbana-Champaign)
A Generative Adversarial Network (GAN) trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. We describe an approach along these lines, with some modifications and refinements, with the following features: (1) on a given class of images, it addresses different linear inverse problems without re-training the neural network; (2) it accelerates the computation substantially as compared to previous GAN-based methods; and (3) it comes with a recovery guarantee. Experiments on several inverse problems demonstrate substantial speedup over earlier GAN-based recovery methods, along with better accuracy.