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

GAN You Do the GAN GAN?