  We propose a simple but effective modification of the discriminators, namely
measure-conditional discriminators, as a plug-and-play module for different
GANs. By taking the generated distributions as part of input so that the target
optimum for the discriminator is stationary, the proposed discriminator is more
robust than the vanilla one. A variant of the measure-conditional discriminator
can also handle multiple target distributions, or act as a surrogate model of
statistical distances such as KL divergence with applications to transfer
learning.
