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Toward Solving Doma...
Toward Solving Domain Adaptation with Limited Source Labeled Data
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- Chen, Kunru, 1993- (author)
- Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)
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- Rögnvaldsson, Thorsteinn, 1963- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Nowaczyk, Sławomir, 1978- (author)
- Högskolan i Halmstad,Centrum för forskning om tillämpade intelligenta system (CAISR)
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- Pashami, Sepideh, 1985- (author)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Klang, Jonas (author)
- Toyota Material Handling Manufacturing Sweden AB, Mjölby, Sweden
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- Sternelov, Gustav (author)
- Toyota Material Handling Manufacturing Sweden AB, Mjölby, Sweden
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(creator_code:org_t)
- Piscataway, NJ : IEEE Computer Society, 2023
- 2023
- English.
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In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW). - Piscataway, NJ : IEEE Computer Society. - 9798350381641 ; , s. 1240-1246
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Activity Recognition
- DANN
- Domain Adaptation
- Limited Data
- Pseudo-label
- Time-Series
Publication and Content Type
- ref (subject category)
- kon (subject category)
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