  Generative Adversarial Networks (GANs), as a framework for estimating
generative models via an adversarial process, have attracted huge attention and
have proven to be powerful in a variety of tasks. However, training GANs is
well known for being delicate and unstable, partially caused by its sig- moid
cross entropy loss function for the discriminator. To overcome such a problem,
many researchers directed their attention on various ways to measure how close
the model distribution and real distribution are and have applied dif- ferent
metrics as their objective functions. In this paper, we propose a novel
framework to train GANs based on distance metric learning and we call it Metric
Learning-based Gener- ative Adversarial Network (MLGAN). The discriminator of
MLGANs can dynamically learn an appropriate metric, rather than a static one,
to measure the distance between generated samples and real samples. Afterwards,
MLGANs update the generator under the newly learned metric. We evaluate our ap-
proach on several representative datasets and the experimen- tal results
demonstrate that MLGANs can achieve superior performance compared with several
existing state-of-the-art approaches. We also empirically show that MLGANs
could increase the stability of training GANs.
