  Solving inverse problems continues to be a challenge in a wide array of
applications ranging from deblurring, image inpainting, source separation etc.
Most existing techniques solve such inverse problems by either explicitly or
implicitly finding the inverse of the model. The former class of techniques
require explicit knowledge of the measurement process which can be unrealistic,
and rely on strong analytical regularizers to constrain the solution space,
which often do not generalize well. The latter approaches have had remarkable
success in part due to deep learning, but require a large collection of
source-observation pairs, which can be prohibitively expensive. In this paper,
we propose an unsupervised technique to solve inverse problems with generative
adversarial networks (GANs). Using a pre-trained GAN in the space of source
signals, we show that one can reliably recover solutions to under determined
problems in a `blind' fashion, i.e., without knowledge of the measurement
process. We solve this by making successive estimates on the model and the
solution in an iterative fashion. We show promising results in three
challenging applications -- blind source separation, image deblurring, and
recovering an image from its edge map, and perform better than several
baselines.
