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

TL;DR

This paper reviews different GAN variants, analyzing their performance in generating and extracting features from clinical data, highlighting their diverse traits and improvements.

Contribution

It categorizes and compares various GAN types based on their shared characteristics, providing insights into their performance in clinical data applications.

Findings

GAN variants show diverse performance in clinical data tasks

Categorization helps understand strengths and weaknesses of different GANs

Review highlights potential for improved generative models in healthcare

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.