Unrolled Generative Adversarial Networks

Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein

Abstract: We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of the discriminator, which is often unstable and leads to poor solutions. We show how this technique solves the common problem of mode collapse, stabilizes training of GANs with complex recurrent generators, and increases diversity and coverage of the data distribution by the generator.

TL;DR: We introduce a method to stabilize Generative Adversarial Networks by defining the generator objective with respect to an unrolled optimization of the discriminator.

Conflicts: google.com

Keywords: Deep learning, Unsupervised Learning, Optimization

Community Implementations: [ 8 code implementations](https://www.catalyzex.com/paper/arxiv:1611.02163/code)