  Phase-retrieval techniques aim to recover the original signal from just the
modulus of its Fourier transform, which is usually much easier to measure than
its phase, but the standard iterative techniques tend to fail if only part of
the modulus information is available. We show that a neural network can be
trained to perform phase retrieval using only incomplete information, and we
discuss advantages and limitations of this approach.
