  Solving inverse problems continues to be a central challenge in computer
vision. Existing techniques either explicitly construct an inverse mapping
using prior knowledge about the corruption, or learn the inverse directly using
a large collection of examples. However, in practice, the nature of corruption
may be unknown, and thus it is challenging to regularize the problem of
inferring a plausible solution. On the other hand, collecting task-specific
training data is tedious for known corruptions and impossible for unknown ones.
We present MimicGAN, an unsupervised technique to solve general inverse
problems based on image priors in the form of generative adversarial networks
(GANs). Using a GAN prior, we show that one can reliably recover solutions to
underdetermined inverse problems through a surrogate network that learns to
mimic the corruption at test time. Our system successively estimates the
corruption and the clean image without the need for supervisory training, while
outperforming existing baselines in blind image recovery. We also demonstrate
that MimicGAN improves upon recent GAN-based defenses against adversarial
attacks and represents one of the strongest test-time defenses available today.
