  Modern Generative Adversarial Networks (GANs) generate realistic images
remarkably well. Previous work has demonstrated the feasibility of
"GAN-classifiers" that are distinct from the co-trained discriminator, and
operate on images generated from a frozen GAN. That such classifiers work at
all affirms the existence of "knowledge gaps" (out-of-distribution artifacts
across samples) present in GAN training. We iteratively train GAN-classifiers
and train GANs that "fool" the classifiers (in an attempt to fill the knowledge
gaps), and examine the effect on GAN training dynamics, output quality, and
GAN-classifier generalization. We investigate two settings, a small DCGAN
architecture trained on low dimensional images (MNIST), and StyleGAN2, a SOTA
GAN architecture trained on high dimensional images (FFHQ). We find that the
DCGAN is unable to effectively fool a held-out GAN-classifier without
compromising the output quality. However, StyleGAN2 can fool held-out
classifiers with no change in output quality, and this effect persists over
multiple rounds of GAN/classifier training which appears to reveal an ordering
over optima in the generator parameter space. Finally, we study different
classifier architectures and show that the architecture of the GAN-classifier
has a strong influence on the set of its learned artifacts.
