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arXiv · 2206.08640

Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift

Andreas Klaß·Sven M. Lorenz·Martin W. Lauer-Schmaltz·David Rügamer·Bernd Bischl·Christopher Mutschler·Felix Ott
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Abstract

For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for computer vision applications, UQ methods for spatio-temporal data are less studied. In this paper, we focus on models for online handwriting recognition, one particular type of spatio-temporal data. The data is observed from a sensor-enhanced pen with the goal to classify written characters. We conduct a broad evaluation of aleatoric (data) and epistemic (model) UQ based on two prominent techniques for Bayesian inference, Stochastic Weight Averaging-Gaussian (SWAG) and Deep Ensembles. Next to a better understanding of the model, UQ techniques can detect out-of-distribution data and domain shifts when combining right-handed and left-handed writers (an underrepresented group).

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Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott. 2022-06-17. Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift. https://arxiv.org/abs/2206.08640

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