  In this paper, we propose the application of conditional generative
adversarial networks to solve various phase retrieval problems. We show that
including knowledge of the measurement process at training time leads to an
optimization at test time that is more robust to initialization than existing
approaches involving generative models. In addition, conditioning the generator
network on the measurements enables us to achieve much more detailed results.
We empirically demonstrate that these advantages provide meaningful solutions
to the Fourier and the compressive phase retrieval problem and that our method
outperforms well-established projection-based methods as well as existing
methods that are based on neural networks. Like other deep learning methods,
our approach is very robust to noise and can therefore be very useful for
real-world applications.
