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STDGAN: ResBlock Based Generative Adversarial Nets Using Spectral Normalization and Two Different Discriminators

Generative adversarial network (GAN) is a powerful generative model. However, it suffers from two key problems, which are convergence and mode collapse. To overcome these drawbacks, this paper presents a novel architecture of GAN, called STDGAN, which consists of one generator and two different discriminators. With the fact that GAN is the analogy of a minimax game, the proposed architecture is as follows. The generator G aims to produce realistic-looking samples to fool both of two discriminators. The first discriminator D1 rewards high scores for the samples from the data distribution, while the second one D2 favors the samples from the generator conversely. Specifically, the minibatch discrimination and Spectral Normalization (SN) are first adopted in D1. Then, based on the ResBlock architecture, Spectral Normalization (SN) and Scaled Exponential Linear Units (SELU) are adopted in the first and last half layers of D2 respectively. In particular, a novel loss function is designed to optimize the STDGAN by minimizing the KL divergence. Extensive experiments on CIFAR-10/100 and ImageNet datasets demonstrate that the proposed STDGAN can effectively solve the problems of convergence and mode collapse and obtain the higher inception score (IS) and lower Frechet Inception Distance (FID) compared with other state-of-the-art GANs.