Optimal transport for deep generative models: state of the art and research challenges

Viet Huynh, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Joint Conference on Artificial Intelligence
EditorsZhi-Hua Zhou
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages4450-4457
Number of pages8
ISBN (Electronic)9780999241196
DOIs
Publication statusPublished - 2021
EventInternational Joint Conference on Artificial Intelligence 2021 - Virtual, Montreal, Canada
Duration: 19 Aug 202127 Aug 2021
Conference number: 30th
https://www.ijcai.org/proceedings/2021/ (Proceedings)
https://ijcai-21.org (Website)

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2021
Abbreviated titleIJCAI 2021
Country/TerritoryCanada
CityMontreal
Period19/08/2127/08/21
Internet address

Keywords

  • Machine learning
  • General

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