**arXiv ID:** 2009.09579 **Authors:** Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim **Published:** 2020-09-21T02:18:58Z **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...
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# On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
**arXiv ID:** 2009.09579
**Authors:** Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim
**Published:** 2020-09-21T02:18:58Z
**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.
## Skill Description
This skill is generated from the arXiv paper: On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study (2009.09579).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2009.09579](http://arxiv.org/abs/2009.09579v1)
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