A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications

Simmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter; Rosenthal, Matthias; Stadelmann, Thilo

doi:10.1109/SDS51136.2021.00012

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22256

Full metadata record

DC Field	Value	Language
dc.contributor.author	Simmler, Niclas	-
dc.contributor.author	Sager, Pascal	-
dc.contributor.author	Andermatt, Philipp	-
dc.contributor.author	Chavarriaga, Ricardo	-
dc.contributor.author	Schilling, Frank-Peter	-
dc.contributor.author	Rosenthal, Matthias	-
dc.contributor.author	Stadelmann, Thilo	-
dc.date.accessioned	2021-04-15T13:36:24Z	-
dc.date.available	2021-04-15T13:36:24Z	-
dc.date.issued	2021-06-09	-
dc.identifier.isbn	978-1-6654-3874-2	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/22256	-
dc.description	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	de_CH
dc.description.abstract	When applying deep learning methods in an industrial vision application, they often fall short of the performance shown in a clean and controlled lab environment due to data quality issues. Few would consider the actual labels as a driving factor, yet inaccurate label data can impair model performance significantly. However, being able to mitigate inaccurate or incomplete labels might also be a cost-saver for real-world projects. Here, we survey state-of-the-art deep learning approaches to resolve such missing labels, noisy labels, and partially labeled data in the prospect of an industrial vision application. We systematically present un-, weakly, and semi-supervised approaches from ’A’ like anomaly detection to ’Z’ like zero-shot classification to resolve these challenges by embracing them.	de_CH
dc.language.iso	en	de_CH
dc.publisher	IEEE	de_CH
dc.rights	Licence according to publishing contract	de_CH
dc.subject	Deep learning	de_CH
dc.subject	Computer vision	de_CH
dc.subject	Label quality	de_CH
dc.subject.ddc	006: Spezielle Computerverfahren	de_CH
dc.title	A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Centre for Artificial Intelligence (CAI)	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
zhaw.organisationalunit	Institute of Embedded Systems (InES)	de_CH
dc.identifier.doi	10.1109/SDS51136.2021.00012	de_CH
dc.identifier.doi	10.21256/zhaw-22256	-
zhaw.conference.details	8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	31	de_CH
zhaw.pages.start	26	de_CH
zhaw.publication.status	acceptedVersion	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.title.proceedings	Proceedings of the 8th SDS	de_CH
zhaw.webfeed	Machine Perception and Cognition	de_CH
zhaw.webfeed	Datalab	de_CH
zhaw.webfeed	Intelligent Vision Systems	de_CH
zhaw.funding.zhaw	FWA: Visual Food Waste Analysis for Sustainable Kitchens	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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Simmler, N., Sager, P., Andermatt, P., Chavarriaga, R., Schilling, F.-P., Rosenthal, M., & Stadelmann, T. (2021). A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [Conference paper]. Proceedings of the 8th SDS, 26–31. https://doi.org/10.1109/SDS51136.2021.00012

Simmler, N. et al. (2021) ‘A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications’, in Proceedings of the 8th SDS. IEEE, pp. 26–31. Available at: https://doi.org/10.1109/SDS51136.2021.00012.

N. Simmler et al., “A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications,” in Proceedings of the 8th SDS, Jun. 2021, pp. 26–31. doi: 10.1109/SDS51136.2021.00012.

SIMMLER, Niclas, Pascal SAGER, Philipp ANDERMATT, Ricardo CHAVARRIAGA, Frank-Peter SCHILLING, Matthias ROSENTHAL und Thilo STADELMANN, 2021. A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications. In: Proceedings of the 8th SDS. Conference paper. IEEE. 9 Juni 2021. S. 26–31. ISBN 978-1-6654-3874-2

Simmler, Niclas, Pascal Sager, Philipp Andermatt, Ricardo Chavarriaga, Frank-Peter Schilling, Matthias Rosenthal, and Thilo Stadelmann. 2021. “A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications.” Conference paper. In Proceedings of the 8th SDS, 26–31. IEEE. https://doi.org/10.1109/SDS51136.2021.00012.

Simmler, Niclas, et al. “A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications.” Proceedings of the 8th SDS, IEEE, 2021, pp. 26–31, https://doi.org/10.1109/SDS51136.2021.00012.