Owing to the wide range of applications in various fields, generative models have become increasingly popular. However, they do not handle spatio-temporal features well. Inspired by the recent advances in these models, this paper designs a distributed spatio-temporal generative adversarial network (STGAN-D) that, given some initial data and random noise, generates a consecutive sequence of spatio-temporal samples which have a logical relationship. This paper builds a spatio-temporal discriminator to distinguish whether the samples generated by the generator meet the requirements for time and space coherence, and builds a controller for distributed training of the network gradient updated to separate the model training and parameter updating, to improve the network training rate. The model is trained on the skeletal dataset and the traffic dataset. In contrast to traditional generative adversarial networks (GANs), the proposed STGAN-D can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse. In addition, this paper shows that the proposed model can generate different styles of spatio-temporal samples given different random noise inputs, and the controller can improve the network training rate. This model will extend the potential range of applications of GANs to areas such as traffic information simulation and multi-agent adversarial simulation.

中文翻译：

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分布式时空生成对抗网络

由于在各个领域的广泛应用，生成模型变得越来越流行。然而，它们不能很好地处理时空特征。受这些模型的最新进展启发，本文设计了一个分布式时空生成对抗网络 (STGAN-D)，该网络在给定一些初始数据和随机噪声的情况下，生成具有逻辑关系的连续时空样本序列。本文构建了一个时空判别器来区分生成器生成的样本是否满足时间和空间相干性的要求，并构建了一个控制器对更新的网络梯度进行分布式训练，将模型训练和参数更新分开，以提高网络训练率。该模型在骨架数据集和交通数据集上进行训练。与传统的生成对抗网络 (GAN) 相比，所提出的 STGAN-D 可以生成具有相应空间和时间特征的逻辑连贯样本，同时避免模式崩溃。此外，本文表明，所提出的模型可以在给定不同的随机噪声输入的情况下生成不同风格的时空样本，并且控制器可以提高网络训练率。该模型将 GAN 的潜在应用范围扩展到交通信息模拟和多代理对抗模拟等领域。本文表明，所提出的模型可以在给定不同的随机噪声输入的情况下生成不同风格的时空样本，并且控制器可以提高网络训练率。该模型将 GAN 的潜在应用范围扩展到交通信息模拟和多代理对抗模拟等领域。本文表明，所提出的模型可以在给定不同的随机噪声输入的情况下生成不同风格的时空样本，并且控制器可以提高网络训练率。该模型将 GAN 的潜在应用范围扩展到交通信息模拟和多代理对抗模拟等领域。