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https://doi.org/10.21256/zhaw-22256| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications |
| Autor/-in: | Simmler, Niclas Sager, Pascal Andermatt, Philipp Chavarriaga, Ricardo Schilling, Frank-Peter Rosenthal, Matthias Stadelmann, Thilo |
| et. al: | No |
| DOI: | 10.1109/SDS51136.2021.00012 10.21256/zhaw-22256 |
| Tagungsband: | Proceedings of the 8th SDS |
| Seite(n): | 26 |
| Seiten bis: | 31 |
| Angaben zur Konferenz: | 8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021 |
| Erscheinungsdatum: | 9-Jun-2021 |
| Verlag / Hrsg. Institution: | IEEE |
| ISBN: | 978-1-6654-3874-2 |
| Sprache: | Englisch |
| Schlagwörter: | Deep learning; Computer vision; Label quality |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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. |
| Weitere Angaben: | © 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22256 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Centre for Artificial Intelligence (CAI) Institut für Informatik (InIT) Institute of Embedded Systems (InES) |
| Publiziert im Rahmen des ZHAW-Projekts: | FWA: Visual Food Waste Analysis for Sustainable Kitchens |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Simmler-etal_Learning-methods-labels-industrial-vision-applications_SDS.pdf | Accepted Version | 137.43 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
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