Generative Adversarial Networks for Spatio-temporal Data: A Survey

Understand

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.