Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
- Authors
- Park, Sung Woo; Shu, Dong Wook; Kwon, Junseok
- Issue Date
- Jul-2021
- Publisher
- JMLR-JOURNAL MACHINE LEARNING RESEARCH
- Citation
- INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, v.139
- Journal Title
- INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139
- Volume
- 139
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/57834
- ISSN
- 2640-3498
- 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.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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