S4: Self-Supervised learning of Spatiotemporal Similarity

Gleb Tkachev, Steffen Frey, Thomas Ertl

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
115 Downloads (Pure)

Abstract

We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.
Original languageEnglish
Pages (from-to)4713-4727
Number of pages15
JournalIEEE Transactions on Visualization and Computer Graphics
Volume28
Issue number12
Early online date2-Aug-2021
DOIs
Publication statusPublished - 1-Dec-2022

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