  Generative Adversarial Networks have become one of the most studied
frameworks for unsupervised learning due to their intuitive formulation. They
have also been shown to be capable of generating convincing examples in limited
domains, such as low-resolution images. However, they still prove difficult to
train in practice and tend to ignore modes of the data generating distribution.
Quantitatively capturing effects such as mode coverage and more generally the
quality of the generative model still remain elusive. We propose Generative
Adversarial Parallelization, a framework in which many GANs or their variants
are trained simultaneously, exchanging their discriminators. This eliminates
the tight coupling between a generator and discriminator, leading to improved
convergence and improved coverage of modes. We also propose an improved variant
of the recently proposed Generative Adversarial Metric and show how it can
score individual GANs or their collections under the GAP model.
