dc.contributor.author	Bäuerle, Alex	en_US
dc.contributor.author	Neumann, Heiko	en_US
dc.contributor.author	Ropinski, Timo	en_US
dc.contributor.editor	Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana	en_US
dc.date.accessioned	2020-05-24T13:00:24Z
dc.date.available	2020-05-24T13:00:24Z
dc.date.issued	2020
dc.description.abstract	Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time-consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well-known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.	en_US
dc.description.number	3
dc.description.sectionheaders	Machine Learning
dc.description.seriesinformation	Computer Graphics Forum
dc.description.volume	39
dc.identifier.doi	10.1111/cgf.13973
dc.identifier.issn	1467-8659
dc.identifier.pages	195-205
dc.identifier.uri	https://doi.org/10.1111/cgf.13973
dc.identifier.uri	https://diglib.eg.org:443/handle/10.1111/cgf13973
dc.publisher	The Eurographics Association and John Wiley & Sons Ltd.	en_US
dc.rights	Attribution 4.0 International License
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	]
dc.subject	Information systems
dc.subject	Expert systems
dc.subject	Human centered computing
dc.subject	User centered design
dc.subject	Information visualization
dc.title	Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks	en_US

Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks

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