Topological Autoencoders
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Rights / License
Abstract
We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders. Using persistent homology, a technique from topological data analysis, we calculate topological signatures of both the input and latent space to derive a topological loss term. Under weak theoretical assumptions, we construct this loss in a differentiable manner, such that the encoding learns to retain multi-scale connectivity information. We show that our approach is theoretically well-founded and that it exhibits favourable latent representations on a synthetic manifold as well as on real-world image data sets, while preserving low reconstruction errors.
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Publication status
published
Book title
Proceedings of the 37th International Conference on Machine Learning
Journal / series
Volume
119
Pages / Article No.
7045 - 7054
Publisher
PMLR
Event
37th International Conference on Machine Learning (ICML 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09486 - Borgwardt, Karsten M. (ehemalig) / Borgwardt, Karsten M. (former)
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
155913 - Significant Pattern Mining (SNF)