  This paper defines a positive and unlabeled classification problem for
standard GANs, which then leads to a novel technique to stabilize the training
of the discriminator in GANs. Traditionally, real data are taken as positive
while generated data are negative. This positive-negative classification
criterion was kept fixed all through the learning process of the discriminator
without considering the gradually improved quality of generated data, even if
they could be more realistic than real data at times. In contrast, it is more
reasonable to treat the generated data as unlabeled, which could be positive or
negative according to their quality. The discriminator is thus a classifier for
this positive and unlabeled classification problem, and we derive a new
Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality
the proposed model will achieve and the equivalent optimization goal.
Empirically, we find that PUGAN can achieve comparable or even better
performance than those sophisticated discriminator stabilization methods.
