On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study

Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim

Abstract: Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction. Various types of GANs are being researched with different insights, resulting in a diverse family of GANs with a better performance in each generation. This review focuses on various GANs categorized by their common traits.