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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: 10.21256/zhaw-22256
Erschienen in: Proceedings of the 8th SDS
Angaben zur Konferenz: 8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021
Erscheinungsdatum: 9-Jun-2021
Verlag / Hrsg. Institution: IEEE
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.
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Centre for Artificial Intelligence (CAI)
Institut für Angewandte Informationstechnologie (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

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