论文标题
时空数据的生成对抗网络：调查
Generative Adversarial Networks for Spatio-temporal Data: A Survey
论文作者
论文摘要
生成的对抗网络（GAN）在计算机视觉区域中产生逼真的图像方面取得了巨大的成功。最近，基于GAN的技术对于基于时空的应用程序（例如轨迹预测，事件生成和时间序列数据插补）是有希望的。虽然已经提出了对计算机视觉中gan的几次评论，但没有人考虑解决与时空数据相关的实际应用和挑战。在本文中，我们对gan的最新发展进行了时空数据进行了全面综述。我们总结了流行的GAN体系结构在时空数据中的应用以及用gan评估时空应用程序性能的常见实践。最后，我们指出了未来的研究指示，以使该领域的研究人员受益。
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.
