Optimal Transport for Deep Generative Models: State of the Art and Research Challenges

Huynh, Viet; Phung, Dinh; Zhao, He

doi:10.24963/ijcai.2021/607

Optimal Transport for Deep Generative Models: State of the Art and Research Challenges

Viet Huynh, Dinh Phung, He Zhao

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4450-4457.

https://doi.org/10.24963/ijcai.2021/607

Keywords

Machine learning: General

Abstract

Optimal transport has a long history in mathematics which was proposed by Gaspard Monge in the eighteenth century (Monge, 1781). However, until recently, advances in optimal transport theory pave the way for its use in the AI community, particularly for formulating deep generative models. In this paper, we provide a comprehensive overview of the literature in the field of deep generative models using optimal transport theory with an aim of providing a systematic review as well as outstanding problems and more importantly, open research opportunities to use the tools from the established optimal transport theory in the deep generative model domain.

Viet Huynh, Dinh Phung, and He Zhao. 2021. Optimal Transport for Deep Generative Models: State of the Art and Research Challenges. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4450–4457. https://doi.org/10.24963/ijcai.2021/607

@inproceedings{ijcai2021p607,
  title     = {Optimal Transport for Deep Generative Models: State of the Art and Research Challenges},
  author    = {Huynh, Viet and Phung, Dinh and Zhao, He},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4450--4457},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/607},
  url       = {https://doi.org/10.24963/ijcai.2021/607},
}

TY  - CPAPER
TI  - Optimal Transport for Deep Generative Models: State of the Art and Research Challenges
AU  - Huynh, Viet
AU  - Phung, Dinh
AU  - Zhao, He
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4450
EP  - 4457
DO  - 10.24963/ijcai.2021/607
UR  - https://doi.org/10.24963/ijcai.2021/607
ER  -