  Generative moment matching network (GMMN) is a deep generative model that
differs from Generative Adversarial Network (GAN) by replacing the
discriminator in GAN with a two-sample test based on kernel maximum mean
discrepancy (MMD). Although some theoretical guarantees of MMD have been
studied, the empirical performance of GMMN is still not as competitive as that
of GAN on challenging and large benchmark datasets. The computational
efficiency of GMMN is also less desirable in comparison with GAN, partially due
to its requirement for a rather large batch size during the training. In this
paper, we propose to improve both the model expressiveness of GMMN and its
computational efficiency by introducing adversarial kernel learning techniques,
as the replacement of a fixed Gaussian kernel in the original GMMN. The new
approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN.
The new distance measure in MMD GAN is a meaningful loss that enjoys the
advantage of weak topology and can be optimized via gradient descent with
relatively small batch sizes. In our evaluation on multiple benchmark datasets,
including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN
significantly outperforms GMMN, and is competitive with other representative
GAN works.
