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

21 September 2020
Jaesung Yoo
Jeman Park
An Wang
David A. Mohaisen
Joongheon Kim
    MedIm
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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.

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