The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization
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Cites
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603
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
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Ensemble of exemplar-SVMs for object detection and beyond
Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126229 · Cited by 865
Domain Generalization via Invariant Feature Representation
Muandet, K., Balduzzi, D., & Schölkopf, B. (2013) · arXiv · doi:10.48550/arxiv.1301.2115 · Cited by 771
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
Bruzzone, L., & Marconcini, M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.57 · Cited by 591 · cited 1×
“The work in [3] developed an approach to iteratively learn the SVM classifier by labeling the unlabeled target samples and simultaneously removing some labeled samples in the source domain.”
Method
Cited by
Domain Generalization: A Survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195549 · Cited by 1,315
Learning to Generalize: Meta-Learning for Domain Generalization
Li, D., Yang, Y., Song, Y.-Z., & Hospedales, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11596 · Cited by 1,251
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046
Unified Deep Supervised Domain Adaptation and Generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.609 · Cited by 803
Domain Generalization for Object Recognition with Multi-task Autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.293 · Cited by 664
Metadata
- Authors
- Xu, Z., Li, W., Niu, L., & Xu, D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 628-643
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W96659543
- Cited by
- 190
- References
- 7
- FWCI
- 7.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. https://app.cochat.ai/papers/10.1007/978-3-319-10578-9_41
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- Xu, Z., Li, W., Niu, L., & Xu, D. (2014). Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-10578-9_41
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
