Generative Adversarial Networks for Spatio-temporal Data: A Survey

Autor:	Gao, Nan, Xue, Hao, Shao, Wei, Zhao, Sichen, Qin, Kyle Kai, Prabowo, Arian, Rahaman, Mohammad Saiedur, Salim, Flora D.
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Information Retrieval Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
DOI:	10.1145/3474838
Popis:	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 paper, 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. Comment: This paper has been accepted by ACM Transactions on Intelligent Systems and Technology (TIST)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.08903 Zobrazit plný text záznamu View this record from Arxiv