ICML 2021spotlight7 citations
Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
Sung Woo Park, Dong Wook Shu, Junseok Kwon
Abstract
In this paper, we present a novel generative adversarial network (GAN) that can describe Markovian temporal dynamics. To generate stochastic sequential data, we introduce a novel stochastic differential equation-based conditional generator and spatial-temporal constrained discriminator networks. To stabilize the learning dynamics of the min-max type of the GAN objective function, we propose well-posed constraint terms for both networks. We also propose a novel conditional Markov Wasserstein distance to induce a pathwise Wasserstein distance. The experimental results demonstrate that our method outperforms state-of-the-art methods using several different types of data.
BibTeX
@InProceedings{pmlr-v139-park21d,
title = {Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation},
author = {Park, Sung Woo and Shu, Dong Wook and Kwon, Junseok},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {8413--8421},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/park21d/park21d.pdf},
url = {https://proceedings.mlr.press/v139/park21d.html},
abstract = {In this paper, we present a novel generative adversarial network (GAN) that can describe Markovian temporal dynamics. To generate stochastic sequential data, we introduce a novel stochastic differential equation-based conditional generator and spatial-temporal constrained discriminator networks. To stabilize the learning dynamics of the min-max type of the GAN objective function, we propose well-posed constraint terms for both networks. We also propose a novel conditional Markov Wasserstein distance to induce a pathwise Wasserstein distance. The experimental results demonstrate that our method outperforms state-of-the-art methods using several different types of data.}
}