Unsupervised hyperbolic representation learning via message passing auto-encoders

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Colleges, School and Institutes

External organisations

  • Seoul National University


Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE

Bibliographic note

Not yet published as of 08/06/2021.


Original languageEnglish
Title of host publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publication statusAccepted/In press - 1 Mar 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Nashville, United States
Duration: 21 Jun 202124 Jun 2021

Publication series

NameProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
ISSN (Electronic)2575-7075


Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2021
CountryUnited States