Before revision:  We apply our algorithm to train GANs on Gaussian mixtures, MNIST and CIFAR-10. We observe that our algorithm trains stably and avoids mode collapse, while achieving a training time per iteration and memory requirement similar to gradient descent-ascent.
After revision:  We apply our algorithm to train GANs on synthetic and real-world datasets, does not cycle, results in GANs that seem to avoid mode collapse, while achieving a training time per iteration and memory requirement similar to gradient descent-ascent.