S4: Self-Supervised learning of Spatiotemporal Similarity

Gleb Tkachev, Steffen Frey, Thomas Ertl

OnderzoeksoutputAcademicpeer review

2 Citaten (Scopus)
10 Downloads (Pure)


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.
Originele taal-2English
Pagina's (van-tot)4713-4727
Aantal pagina's15
TijdschriftIEEE Transactions on Visualization and Computer Graphics
Nummer van het tijdschrift12
Vroegere onlinedatum2-aug.-2021
StatusPublished - 1-dec.-2022

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