Generative Adversarial Networks for Spatio-temporal Data: A Survey

TL;DR

This survey reviews recent advances in applying Generative Adversarial Networks to spatio-temporal data, highlighting architectures, evaluation practices, and future research directions.

Contribution

It provides the first comprehensive overview of GAN applications, challenges, and evaluation methods specifically for spatio-temporal data.

Findings

Summarizes popular GAN architectures for spatio-temporal data.

Discusses evaluation practices for spatio-temporal GAN applications.

Identifies future research directions in the field.

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 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.