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

Neural and Evolutionary Computing 2020-09-22 v1 Artificial Intelligence Machine Learning

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.

Keywords

Cite

@article{arxiv.2009.09579,
  title  = {On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study},
  author = {Jaesung Yoo and Jeman Park and An Wang and David Mohaisen and Joongheon Kim},
  journal= {arXiv preprint arXiv:2009.09579},
  year   = {2020}
}
R2 v1 2026-06-23T18:40:37.691Z