Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22256
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dc.contributor.authorSimmler, Niclas-
dc.contributor.authorSager, Pascal-
dc.contributor.authorAndermatt, Philipp-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorSchilling, Frank-Peter-
dc.contributor.authorRosenthal, Matthias-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2021-04-15T13:36:24Z-
dc.date.available2021-04-15T13:36:24Z-
dc.date.issued2021-06-09-
dc.identifier.urihttps://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.abstractWhen 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.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofProceedings of the 8th SDSde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDeep learningde_CH
dc.subjectComputer visionde_CH
dc.subjectLabel qualityde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleA survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applicationsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitut für Angewandte Informationstechnologie (InIT)de_CH
zhaw.organisationalunitInstitute of Embedded Systems (InES)de_CH
dc.identifier.doi10.21256/zhaw-22256-
zhaw.conference.details8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedComputer Vision, Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawFWA: Visual Food Waste Analysis for Sustainable Kitchensde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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