Improving the Speed and Quality of GAN by Adversarial Training
This addresses the problem of inefficient and resource-intensive GAN training for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackled the slow convergence and limited expressiveness of GANs by developing FastGAN, an adversarial training algorithm that improved generation quality on benchmarks like CIFAR10 and ImageNet, reducing training time and enabling use with only 2-4 GPUs.
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.