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. * Work done as a member of the Google Brain Residency program (g.co/brainresidency) † Work completed as part of a Google Brain internship
To make the generator and the discriminator more balanced, Metz et al. REF created an unrolled objective function to enhance the generator.
Unrolled Generative Adversarial Networks
ArXiv
ArXiv
Mathematics Computer Science