  In order to alleviate the notorious mode collapse phenomenon in generative
adversarial networks (GANs), we propose a novel training method of GANs in
which certain fake samples are considered as real ones during the training
process. This strategy can reduce the gradient value that generator receives in
the region where gradient exploding happens. We show the process of an
unbalanced generation and a vicious circle issue resulted from gradient
exploding in practical training, which explains the instability of GANs. We
also theoretically prove that gradient exploding can be alleviated by
penalizing the difference between discriminator outputs and fake-as-real
consideration for very close real and fake samples. Accordingly, Fake-As-Real
GAN (FARGAN) is proposed with a more stable training process and a more
faithful generated distribution. Experiments on different datasets verify our
theoretical analysis.
