  We propose a framework for adversarial training that relies on a sample
rather than a single sample point as the fundamental unit of discrimination.
Inspired by discrepancy measures and two-sample tests between probability
distributions, we propose two such distributional adversaries that operate and
predict on samples, and show how they can be easily implemented on top of
existing models. Various experimental results show that generators trained with
our distributional adversaries are much more stable and are remarkably less
prone to mode collapse than traditional models trained with pointwise
prediction discriminators. The application of our framework to domain
adaptation also results in considerable improvement over recent
state-of-the-art.
