  Generative Adversarial Networks (GANs) have become a dominant class of
generative models. In recent years, GAN variants have yielded especially
impressive results in the synthesis of a variety of forms of data. Examples
include compelling natural and artistic images, textures, musical sequences,
and 3D object files. However, one obvious synthesis candidate is missing. In
this work, we answer one of deep learning's most pressing questions: GAN you do
the GAN GAN? That is, is it possible to train a GAN to model a distribution of
GANs? We release the full source code for this project under the MIT license.
