SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
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Date
2022
Authors
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Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio-temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio-temporal ensemble data without re-training. The latent-space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large-scale spatio-temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in-depth basic understanding of the underlying temporal evolution of a given ensemble.
Description
@article{10.1111:cgf.14548,
journal = {Computer Graphics Forum},
title = {{SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles}},
author = {Huesmann, Karim and Linsen, Lars},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14548}
}