  Generative adversarial networks (GAN) have shown remarkable results in image
generation tasks. High fidelity class-conditional GAN methods often rely on
stabilization techniques by constraining the global Lipschitz continuity. Such
regularization leads to less expressive models and slower convergence speed;
other techniques, such as the large batch training, require unconventional
computing power and are not widely accessible. In this paper, we develop an
efficient algorithm, namely FastGAN (Free AdverSarial Training), to improve the
speed and quality of GAN training based on the adversarial training technique.
We benchmark our method on CIFAR10, a subset of ImageNet, and the full ImageNet
datasets. We choose strong baselines such as SNGAN and SAGAN; the results
demonstrate that our training algorithm can achieve better generation quality
(in terms of the Inception score and Frechet Inception distance) with less
overall training time. Most notably, our training algorithm brings ImageNet
training to the broader public by requiring 2-4 GPUs.
