  Building on top of the success of generative adversarial networks (GANs),
conditional GANs attempt to better direct the data generation process by
conditioning with certain additional information. Inspired by the most recent
AC-GAN, in this paper we propose a fast-converging conditional GAN (FC-GAN). In
addition to the real/fake classifier used in vanilla GANs, our discriminator
has an advanced auxiliary classifier which distinguishes each real class from
an extra `fake' class. The `fake' class avoids mixing generated data with real
data, which can potentially confuse the classification of real data as AC-GAN
does, and makes the advanced auxiliary classifier behave as another real/fake
classifier. As a result, FC-GAN can accelerate the process of differentiation
of all classes, thus boost the convergence speed. Experimental results on image
synthesis demonstrate our model is competitive in the quality of images
generated while achieving a faster convergence rate.
