  Generative Adversarial Networks (GANs) have become a very popular tool for
implicitly learning high-dimensional probability distributions. Several
improvements have been made to the original GAN formulation to address some of
its shortcomings like mode collapse, convergence issues, entanglement, poor
visual quality etc. While a significant effort has been directed towards
improving the visual quality of images generated by GANs, it is rather
surprising that objective image quality metrics have neither been employed as
cost functions nor as regularizers in GAN objective functions. In this work, we
show how a distance metric that is a variant of the Structural SIMilarity
(SSIM) index (a popular full-reference image quality assessment algorithm), and
a novel quality aware discriminator gradient penalty function that is inspired
by the Natural Image Quality Evaluator (NIQE, a popular no-reference image
quality assessment algorithm) can each be used as excellent regularizers for
GAN objective functions. Specifically, we demonstrate state-of-the-art
performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over
CIFAR-10, STL10 and CelebA datasets.
