  In this article, we introduce a new mode for training Generative Adversarial
Networks (GANs). Rather than minimizing the distance of evidence distribution
$\tilde{p}(x)$ and the generative distribution $q(x)$, we minimize the distance
of $\tilde{p}(x_r)q(x_f)$ and $\tilde{p}(x_f)q(x_r)$. This adversarial pattern
can be interpreted as a Turing test in GANs. It allows us to use information of
real samples during training generator and accelerates the whole training
procedure. We even find that just proportionally increasing the size of
discriminator and generator, it succeeds on 256x256 resolution without
adjusting hyperparameters carefully.
