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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.08640 |
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| _version_ | 1866913532372582400 |
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| author | Klaß, Andreas Lorenz, Sven M. Lauer-Schmaltz, Martin W. Rügamer, David Bischl, Bernd Mutschler, Christopher Ott, Felix |
| author_facet | Klaß, Andreas Lorenz, Sven M. Lauer-Schmaltz, Martin W. Rügamer, David Bischl, Bernd Mutschler, Christopher Ott, Felix |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_08640 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift Klaß, Andreas Lorenz, Sven M. Lauer-Schmaltz, Martin W. Rügamer, David Bischl, Bernd Mutschler, Christopher Ott, Felix Computer Vision and Pattern Recognition Artificial Intelligence 62F15 H.1.1 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). |
| title | Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 62F15 H.1.1 |
| url | https://arxiv.org/abs/2206.08640 |