Improved Performance of GANs via Integrating Gradient Penalty with Spectral Normalization

Despite the growing prominence of generative adversarial networks (GANs), improving the performance of GANs is still a challenging problem. To this end, a combination method for training GANs is proposed by coupling spectral normalization with a zero-centered gradient penalty technique (the penalty is done on the inner function of Sigmoid function of discriminator). Particularly, the proposed method not only overcomes the limitations of networks convergence and training instability but also alleviates the mode collapse behavior in GANs. Experimentally, the improved method becomes more competitive compared with some of recent methods on several datasets.