Recent Progress on Generative Adversarial Networks (GANs): A Survey

By Zhaoqing Pan, Wei-Jie Yu, Xiaokai Yi, Asifullah Khan, Feng Yuan, Yuhui Zheng. 2019

552 citations recorded by the source corpus.

Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.

Subfields: Computer Vision and Pattern Recognition, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Zhenan Sun
3. Xi Li
4. Jie Gui
5. Abdul Jabbar
6. Jieping Ye
7. Yonggang Wen
8. Bourahla Omar

In Generative Models

1. Dacheng Tao
2. Jie Gui
3. Jieping Ye
4. Ekram Hossain
5. Yonggang Wen
6. Syed Ali Hassan
7. Bourahla Omar
8. Zhenan Sun

Citations in the corpus

• Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends (2021, background): “There have, however, been many excellent recent surveys on the state of the art (Alqahtani et al., 2019; Hong et al., 2019; Pan et al., 2019; Khursheed et al., 2020), performance (Kurach et al.”
• A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, background): “Generative Adversarial Networks: A Survey and Taxonomy [3] arXiv 2019 3.”
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, background): “Other related work can be found in [11]–[13].”
• Generative artificial intelligence (2023, unknown): “The generator creates realistic data samples, while the discriminator distinguishes between real and generated samples (Pan et al., 2019).”
• Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “Recently, GANs have been extensively used in the applications leveraging ML and DL methods [114]–[116].”
• Machine learning and deep learning (2021, background): “…to revolutionize domains in which continuously new content or novel product configurations are created (e.g., the composition of art and music, design of fashion), or where content is converted from one representation to another (e.g., text to image for product descriptions) (Pan et al. 2019).”