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 language | English |
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Title of host publication | Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence |
Editors | Zhi-Hua Zhou |
Place of Publication | Marina del Rey CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 4450-4457 |
Number of pages | 8 |
ISBN (Electronic) | 9780999241196 |
DOIs | |
Publication status | Published - 2021 |
Event | International Joint Conference on Artificial Intelligence 2021 - Virtual, Montreal, Canada Duration: 19 Aug 2021 → 27 Aug 2021 Conference number: 30th https://www.ijcai.org/proceedings/2021/ (Proceedings) https://ijcai-21.org (Website) |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
ISSN (Print) | 1045-0823 |
Conference
Conference | International Joint Conference on Artificial Intelligence 2021 |
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Abbreviated title | IJCAI 2021 |
Country/Territory | Canada |
City | Montreal |
Period | 19/08/21 → 27/08/21 |
Internet address |
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Keywords
- Machine learning
- General