  A Generative Adversarial Network (GAN) was used to investigate the statistics
and properties of voids in a $\Lambda$CDMuniverse. The total number of voids
and the distribution of void sizes is similar in both sets of images and,
within the formal error bars, the mean void properties are consistent with each
other. However, the generated images yield somewhat fewer small voids than do
the simulated images. In addition, the generated images yield far fewer voids
with central density contrast $\sim$ $-$1. Because the generated images yield
fewer of the emptiest voids, the distribution of the mean interior density
contrast is systematically higher for the generated voids than it is for the
simulated voids. The mean radial underdensity profiles of the largest voids are
similar in both sets of images, but systematic differences are apparent. On
small scales (r $< 0.5r_{v}$), the underdensity profiles of the voids in the
generated images exceed those of the voids in the simulated images. On large
scales (r $> 0.5r_{v}$), the underdensity profiles of the voids in the
simulated images exceed those of the voids in the generated images. The
discrepancies between the void properties in the two sets of images are
attributable to the GAN struggling to capture absolute patterns in the data. In
particular, the GAN produces too few pixels with density contrasts $\sim$ $-$1
and too many pixels with density contrasts in the range $\sim$ $-$0.88 to
$\sim$ $-$0.63.
