  Generative Adversarial Networks (GANs) can successfully approximate a
probability distribution and produce realistic samples. However, open questions
such as sufficient convergence conditions and mode collapse still persist. In
this paper, we build on existing work in the area by proposing a novel
framework for training the generator against an ensemble of discriminator
networks, which can be seen as a one-student/multiple-teachers setting. We
formalize this problem within the full-information adversarial bandit
framework, where we evaluate the capability of an algorithm to select mixtures
of discriminators for providing the generator with feedback during learning. To
this end, we propose a reward function which reflects the progress made by the
generator and dynamically update the mixture weights allocated to each
discriminator. We also draw connections between our algorithm and stochastic
optimization methods and then show that existing approaches using multiple
discriminators in literature can be recovered from our framework. We argue that
less expressive discriminators are smoother and have a general coarse grained
view of the modes map, which enforces the generator to cover a wide portion of
the data distribution support. On the other hand, highly expressive
discriminators ensure samples quality. Finally, experimental results show that
our approach improves samples quality and diversity over existing baselines by
effectively learning a curriculum. These results also support the claim that
weaker discriminators have higher entropy improving modes coverage. Keywords:
multiple discriminators, curriculum learning, multiple resolutions
discriminators, multi-armed bandits, generative adversarial networks, smooth
discriminators, multi-discriminator gan training, multiple experts.
