  We investigate training Generative Adversarial Networks, GANs, with less
data. Subsets of the training dataset can express empirical sample diversity
while reducing training resource requirements, e.g. time and memory. We ask how
much data reduction impacts generator performance and gauge the additive value
of generator ensembles. In addition to considering stand-alone GAN training and
ensembles of generator models, we also consider reduced data training on an
evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner
makes GAN training more robust and accurate by exploiting overlapping
neighborhood-based training on a spatial 2D grid. We conduct empirical
experiments on Redux-Lipizzaner using the MNIST and CelebA data sets.
