论文标题
关于生成对抗网络（GAN）变体的性能：临床数据研究
On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
论文作者
论文摘要
生成对抗网络（GAN）是在各种应用程序中的神经网络的有用类型，包括生成模型和特征提取。正在研究各种类型的甘恩，从而产生了各种各样的gan家族，每一代人的性能都更好。这篇综述着重于按其共同特征分类的各种gan。
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.
