  Despite the success of generative adversarial networks (GANs) for image
generation, the trade-off between visual quality and image diversity remains a
significant issue. This paper achieves both aims simultaneously by improving
the stability of training GANs. The key idea of the proposed approach is to
implicitly regularize the discriminator using representative features. Focusing
on the fact that standard GAN minimizes reverse Kullback-Leibler (KL)
divergence, we transfer the representative feature, which is extracted from the
data distribution using a pre-trained autoencoder (AE), to the discriminator of
standard GANs. Because the AE learns to minimize forward KL divergence, our GAN
training with representative features is influenced by both reverse and forward
KL divergence. Consequently, the proposed approach is verified to improve
visual quality and diversity of state of the art GANs using extensive
evaluations.
