Atlas generative models and geodesic interpolation

Research output: Contribution to journalJournal articleResearchpeer-review

Documents

Generative neural networks have a well recognized ability to estimate underlying manifold structure of high dimensional data. However, if a single latent space is used, it is not possible to faithfully represent a manifold with topology different from Euclidean space. In this work we define the general class of Atlas Generative Models (AGMs), models with hybrid discrete-continuous latent space that estimate an atlas on the underlying data manifold together with a partition of unity on the data space. We identify existing examples of models from various popular generative paradigms that fit into this class. Due to the atlas interpretation, ideas from non-linear latent space analysis and statistics, e.g. geodesic interpolation, which has previously only been investigated for models with simply connected latent spaces, may be extended to the entire class of AGMs in a natural way. We exemplify this by generalizing an algorithm for graph based geodesic interpolation to the setting of AGMs, and verify its performance experimentally.
Original languageEnglish
Article number104433
JournalImage and Vision Computing
Volume122
Number of pages23
ISSN0262-8856
DOIs
Publication statusPublished - 2022

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 302814257