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 |
|---|---|
| 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 |
|---|---|
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| ISSN (Print) | 1045-0823 |
Conference
| Conference | International Joint Conference on Artificial Intelligence 2021 |
|---|---|
| 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