The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.
How Generative Adversarial Networks and Their Variants Work: An Overview
Open accessAbstract
Generative Adversarial Networks (GANs) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Specifically, they do not rely on any assumptions about the distribution and can generate real-like samples from latent space in a simple manner. This powerful property allows GANs to be applied to various applications such as image synthesis, image attribute editing, image translation, domain adaptation, and other academic fields. In this article, we discuss the details of GANs for those readers who are familiar with, but do not comprehend GANs deeply or who wish to view GANs from various perspectives. In addition, we explain how GANs operates and the fundamental meaning of various objective functions that have been suggested recently. We then focus on how the GAN can be combined with an autoencoder framework. Finally, we enumerate the GAN variants that are applied to various tasks and other fields for those who are interested in exploiting GANs for their research.
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Cites
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Cited by
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Moustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 721
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Saxena, D., & Cao, J. (2022) · ACM Computing Surveys · doi:10.1145/3446374 · Cited by 689
The relativistic discriminator: a key element missing from standard GAN
Jolicoeur‐Martineau, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00734 · Cited by 532
Metadata
- Authors
- Hong, Y., Hwang, U., Yoo, J., & Yoon, S.
- Venue
- ACM Computing Surveys
- Published
- 2019-02-13
- Volume
- 52
- Issue
- 1
- Pages
- 1-43
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2899901572
- Cited by
- 373
- References
- 138
- FWCI
- 25.03
- Type
- journal article
- Language
- en
- Open access
- green
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