# Improving the Speed and Quality of GAN by Adversarial Training Source: [Improving the Speed and Quality of GAN by Adversarial Training]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:29:00.055Z Authors: Jiachen Zhong, Xuanqing Liu, Cho-Jui Hsieh Published: 2020-08-07T20:21:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Generation on CIFAR10 32x32 (test)](): Image Generation; CIFAR10 32x32 (test); FID: 9.67. ## Original sources and implementation [Original paper]() [PDF]()