The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Unsupervised Visual Domain Adaptation Using Subspace Alignment
Open accessAbstract
In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.
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Cites
Domain Adaptation via Transfer Component Analysis
Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,753
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., & Darrell, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_16 · Cited by 2,847
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247911 · Cited by 2,093
Analysis of Representations for Domain Adaptation
Ben-David, S., Blitzer, J., Crammer, K., & Pereira, F. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0022 · Cited by 2,019
Domain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543
Domain adaptation for object recognition: An unsupervised approach
Gopalan, R., Li, R., & Chellappa, R. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126344 · Cited by 1,053
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Kulis, B., Saenko, K., & Darrell, T. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995702 · Cited by 688
Transfer learning via dimensionality reduction
Pan, S. J., Kwok, J. T.-Y., & Yang, Q. (2008) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 653
Undoing the Damage of Dataset Bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33718-5_12 · Cited by 480
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Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
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
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757
Deep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Deep Hashing Network for Unsupervised Domain Adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.572 · Cited by 2,171
Metadata
- Authors
- Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 2960-2967
- OpenAlex
- W2104068492
- Cited by
- 1,314
- References
- 18
- FWCI
- 38.81
- Type
- conference paper
- Language
- en
- Open access
- green
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- Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T. (2013). Unsupervised Visual Domain Adaptation Using Subspace Alignment. 2013 IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2013.368
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