Generative Adversarial Networks for Spatio-temporal Data: A Survey

Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle K. Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim

Abstract

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

Locations

• ACM Transactions on Intelligent Systems and Technology - View
• arXiv (Cornell University) - View - PDF
• DataCite API - View