# GAN You Do the GAN GAN?

## TL;DR

This paper explores the novel idea of training a GAN to model a distribution of GANs, extending generative modeling capabilities to a higher level of abstraction.

## Contribution

It introduces the concept of a GAN that can generate other GANs, providing the first exploration into modeling distributions of generative models.

## Key findings

- Successfully trained a GAN to generate other GANs
- Demonstrated the potential for higher-level generative modeling
- Released source code for reproducibility

## Abstract

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.