Distributed spatio-temporal generative adversarial networks

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.