  Generative Adversarial Networks (GANs) are a revolutionary class of Deep
Neural Networks (DNNs) that have been successfully used to generate realistic
images, music, text, and other data. However, GAN training presents many
challenges, notably it can be very resource-intensive. A potential weakness in
GANs is that it requires a lot of data for successful training and data
collection can be an expensive process. Typically, the corrective feedback from
discriminator DNNs to generator DNNs (namely, the discriminator's assessment of
the generated example) is calculated using only one real-numbered value (loss).
By contrast, we propose a new class of GAN we refer to as xAI-GAN that
leverages recent advances in explainable AI (xAI) systems to provide a "richer"
form of corrective feedback from discriminators to generators. Specifically, we
modify the gradient descent process using xAI systems that specify the reason
as to why the discriminator made the classification it did, thus providing the
"richer" corrective feedback that helps the generator to better fool the
discriminator. Using our approach, we observe xAI-GANs provide an improvement
of up to 23.18% in the quality of generated images on both MNIST and FMNIST
datasets over standard GANs as measured by Frechet Inception Distance (FID). We
further compare xAI-GAN trained on 20% of the data with standard GAN trained on
100% of data on the CIFAR10 dataset and find that xAI-GAN still shows an
improvement in FID score. Further, we compare our work with Differentiable
Augmentation - which has been shown to make GANs data-efficient - and show that
xAI-GANs outperform GANs trained on Differentiable Augmentation. Moreover, both
techniques can be combined to produce even better results. Finally, we argue
that xAI-GAN enables users greater control over how models learn than standard
GANs.
