An Effective Supplementation of Insufficient Data by Generative Adversarial Networks-Reference-Cited by-同舟云学术

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An Effective Supplementation of Insufficient Data by Generative Adversarial Networks

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
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Author:

Abdulraheem Abdulkabir¹,Jung Im Y.¹

Affiliation:

1. Kyungpook National University,School of Electronic and Electrical Engineering,Daegu,Republic of Korea,41566

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10061849/10061864/10061893.pdf?arnumber=10061893

Reference8 articles.

1. The Fréchet distance between multivariate normal distributions

2. GANs trained by a two time-scale update rule converge to a local Nash equilibrium;heusel;International Conference on Neural Information Processing Systems,2017

3. Are GANs Created Equal? A Large-Scale Study;lucic;International Conference on Neural Information Processing Systems,2018

4. Wasserstein generative adversarial networks;arjovsky;International Conference on Machine Learning,2017

5. Improved Training of Wassersteinin Gans;gulrajani;International Conference on Neural Information Processing Systems,2017

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