  Though recent research has achieved remarkable progress in generating
realistic images with generative adversarial networks (GANs), the lack of
training stability is still a lingering concern of most GANs, especially on
high-resolution inputs and complex datasets. Since the randomly generated
distribution can hardly overlap with the real distribution, training GANs often
suffers from the gradient vanishing problem. A number of approaches have been
proposed to address this issue by constraining the discriminator's capabilities
using empirical techniques, like weight clipping, gradient penalty, spectral
normalization etc. In this paper, we provide a more principled approach as an
alternative solution to this issue. Instead of training the discriminator to
distinguish real and fake input samples, we investigate the relationship
between paired samples by training the discriminator to separate paired samples
from the same distribution and those from different distributions. To this end,
we explore a relation network architecture for the discriminator and design a
triplet loss which performs better generalization and stability. Extensive
experiments on benchmark datasets show that the proposed relation discriminator
and new loss can provide significant improvement on variable vision tasks
including unconditional and conditional image generation and image translation.
