  Standard adversarial training involves two agents, namely a generator and a
discriminator, playing a mini-max game. However, even if the players converge
to an equilibrium, the generator may only recover a part of the target data
distribution, in a situation commonly referred to as mode collapse. In this
work, we present the Domain Partitioning Network (DoPaNet), a new approach to
deal with mode collapse in generative adversarial learning. We employ multiple
discriminators, each encouraging the generator to cover a different part of the
target distribution. To ensure these parts do not overlap and collapse into the
same mode, we add a classifier as a third agent in the game. The classifier
decides which discriminator the generator is trained against for each sample.
Through experiments on toy examples and real images, we show the merits of
DoPaNet in covering the real distribution and its superiority with respect to
the competing methods. Besides, we also show that we can control the modes from
which samples are generated using DoPaNet.
