Generative adversarial networks: introduction and outlook

By Kunfeng Wang, Chao Gou, Yanjie Duan, Yilun Lin, Xinhu Zheng, Fei-yue Wang. IEEE/CAA Journal of Automatica Sinica, 2017

652 citations recorded by the source corpus.

Recently, generative adversarial networks U+0028 GANs U+0029 have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs U+02BC proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs U+02BC advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/60885c61116c5ff5ac61270dcd8d430ca6f3e692

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. Qi She
4. Fei Wang
5. Jie Gui
6. Zhengwei Wang
7. Jiannong Cao
8. Divya Saxena

In Statistical and Nonlinear Physics

1. Dacheng Tao
2. Jie Gui
3. Jieping Ye
4. Divya Saxena
5. Jiannong Cao
6. Simon J. D. Prince
7. Yonggang Wen
8. Tomás Ward

Citations in the corpus

• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, background): “[7] which mainly introduced the progress of GANs before 2017.”
• Generative Adversarial Networks (GANs) (2020, background): “outline major open issues that can be pursued by researchers further. There are a limited number of existing reviews on the topic of GANs. [13] discussed how GANs and stateof-the-art GANs works. [14]–[16] provided a brief introduction of some of the GANs models, while [16] also present development t”
• Normalizing Flows: An Introduction and Review of Current Methods (2020, background): “There are several survey papers for VAEs [55] and GANs [17], [100].”
• Generative Adversarial Networks: A Survey and Taxonomy (2019, background): “Papers [69]–[71] focus on the investigation of the newest development treads and the applications of GANs.”
• Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting (2019, background): “Since their initiation, GAN has been applied to various research field [36], including image and vision computing, speech and language processing, etc. Owing to the powerful modeling ability, GAN is able to create the new data that has the similar distribution of the original data but also remain…”