  Adversarial neural networks solve many important problems in data science,
but are notoriously difficult to train. These difficulties come from the fact
that optimal weights for adversarial nets correspond to saddle points, and not
minimizers, of the loss function. The alternating stochastic gradient methods
typically used for such problems do not reliably converge to saddle points, and
when convergence does happen it is often highly sensitive to learning rates. We
propose a simple modification of stochastic gradient descent that stabilizes
adversarial networks. We show, both in theory and practice, that the proposed
method reliably converges to saddle points, and is stable with a wider range of
training parameters than a non-prediction method. This makes adversarial
networks less likely to "collapse," and enables faster training with larger
learning rates.
