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

Jaesung Yoo; Jeman Park; An Wang; David Mohaisen; Joongheon Kim

doi:10.1109/ICTC49870.2020.9289248

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

Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	100-104
Number of pages	5
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289248
Publication status	Published - 21 Oct 2020
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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10.1109/ICTC49870.2020.9289248

Cite this

Yoo, J., Park, J., Wang, A., Mohaisen, D., & Kim, J. (2020). On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 100-104). Article 9289248 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289248

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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.",

author = "Jaesung Yoo and Jeman Park and An Wang and David Mohaisen and Joongheon Kim",

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doi = "10.1109/ICTC49870.2020.9289248",

language = "English",

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Yoo, J, Park, J, Wang, A, Mohaisen, D & Kim, J 2020, On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289248, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 100-104, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289248

On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study. / Yoo, Jaesung; Park, Jeman; Wang, An et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 100-104 9289248 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Yoo J, Park J, Wang A, Mohaisen D, Kim J. On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society. 2020. p. 100-104. 9289248. (International Conference on ICT Convergence). doi: 10.1109/ICTC49870.2020.9289248

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

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