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Toward Solving Domain Adaptation with Limited Source Labeled Data
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-6420-8316
Halmstad University, School of Information Technology.ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology.ORCID iD: 0000-0003-3272-4145
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2023 (English)In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW) / [ed] Jihe Wang; Yi He, Thang N. Dinh; Christan Grant; Meikang Qiu; Witold Pedrycz, Piscataway, NJ: IEEE Computer Society, 2023, p. 1240-1246Conference paper, Published paper (Refereed)
Abstract [en]

The success of domain adaptation relies on high-quality labeled data from the source domain, which is a luxury setup for applied machine learning problems. This article investigates a particular challenge: the source labeled data are neither plentiful nor sufficiently representative. We studied the challenge of limited data with an industrial application, i.e., forklift truck activity recognition. The task is to develop data-driven methods to recognize forklift usage performed in different warehouses with a large scale of signals collected from the onboard sensors. The preliminary results show that using pseudo-labeled data from the source domain can significantly improve classification performance on the target domain in some tasks. As the real-world problems are much more complex than typical research settings, it is not clearly understood in what circumstance the improvement may occur. Therefore, we provided discussions regarding this phenomenon and shared several inspirations on the difficulty of understanding and debugging domain adaptation problems in practice. © 2023 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2023. p. 1240-1246
Series
Proceedings ... ICDM workshops, E-ISSN 2375-9259
Keywords [en]
Activity Recognition, DANN, Domain Adaptation, Limited Data, Pseudo-label, Time-Series
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52854DOI: 10.1109/ICDMW60847.2023.00161ISI: 001164077500152Scopus ID: 2-s2.0-85186143362ISBN: 9798350381641 (electronic)OAI: oai:DiVA.org:hh-52854DiVA, id: diva2:1843333
Conference
23rd IEEE International Conference on Data Mining Workshops (ICDMW 2023), Shanghai, China, 1-4 December, 2023
Funder
Knowledge Foundation
Note

Funding: The Knowledge Foundation, Halmstad University, and Toyota Material Handling Europe

Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-05-23Bibliographically approved

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Chen, KunruRögnvaldsson, ThorsteinnNowaczyk, SławomirPashami, Sepideh

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