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Building on top of the success of generative adversarial networks (GANs),conditional GANs attempt to better direct the data generation process byconditioning with certain additional information. Inspired by the most recentAC-GAN, in this paper we propose a fast-converging conditional GAN (FC-GAN). Inaddition to the real/fake classifier used in vanilla GANs, our discriminatorhas an advanced auxiliary classifier which distinguishes each real class froman extra `fake' class. The `fake' class avoids mixing generated data with realdata, which can potentially confuse the classification of real data as AC-GANdoes, and makes the advanced auxiliary classifier behave as another real/fakeclassifier. As a result, FC-GAN can accelerate the process of differentiationof all classes, thus boost the convergence speed. Experimental results on imagesynthesis demonstrate our model is competitive in the quality of imagesgenerated while achieving a faster convergence rate.

Fast-converging Conditional Generative Adversarial Networks for Image Synthesis