On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
It provides a structured overview for researchers working with GANs in clinical data applications, but it is incremental as it reviews existing methods without introducing novel findings.
This review categorizes various Generative Adversarial Network (GAN) variants based on their common traits to assess their performance, but it does not present new experimental results or concrete numerical improvements.
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.