The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Domain adaptation from multiple sources via auxiliary classifiers
Abstract
We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target domain by leveraging a set of pre-computed classifiers (referred to as auxiliary/source classifiers) independently learned with the labeled patterns from multiple source domains. We introduce a new data-dependent regularizer based on smoothness assumption into Least-Squares SVM (LS-SVM), which enforces that the target classifier shares similar decision values with the auxiliary classifiers from relevant source domains on the unlabeled patterns of the target domain. In addition, we employ a sparsity regularizer to learn a sparse target classifier. Comprehensive experiments on the challenging TRECVID 2005 corpus demonstrate that DAM outperforms the existing multiple source domain adaptation methods for video concept detection in terms of effectiveness and efficiency.
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Cites
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Correcting Sample Selection Bias by Unlabeled Data
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Domain adaptation with structural correspondence learning
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Working Set Selection Using Second Order Information for Training Support Vector Machines
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Frustratingly Easy Domain Adaptation
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Evaluation campaigns and TRECVid
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Cited by
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806
Return of Frustratingly Easy Domain Adaptation
Sun, B., Feng, J., & Saenko, K. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10306 · Cited by 2,004
Unsupervised Domain Adaptation with Residual Transfer Networks
Long, M., Han, Z., Wang, J., & Jordan, M. I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04433 · Cited by 1,075
Domain Transfer Multiple Kernel Learning
Duan, L., Tsang, I. W., & Xu, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.114 · Cited by 548
Self-citation
Metadata
- Authors
- Duan, L., Tsang, I. W., Xu, D., & Chua, T.-S.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 289-296
- OpenAlex
- W2069057437
- Cited by
- 289
- References
- 26
- FWCI
- 12.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Duan, L., Tsang, I. W., Xu, D., & Chua, T.-S. (2009). Domain adaptation from multiple sources via auxiliary classifiers. Proceedings of the 26th Annual International Conference on Machine Learning. https://doi.org/10.1145/1553374.1553411
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