The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
Open accessAbstract
In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers. It provides a geometrically meaningful guidance to detect target samples that are far from the support of the source and enables efficient distribution alignment in an end-to-end trainable fashion. In the experiments, we validate the effectiveness and genericness of our method on digit and sign recognition, image classification, semantic segmentation, and object detection.
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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
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Metadata
- Authors
- Lee, C.-Y., Batra, T., Baig, M. H., & Ulbricht, D.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 10277-10287
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2921342716
- Cited by
- 577
- References
- 72
- FWCI
- 29.55
- Type
- conference paper
- Language
- en
- Open access
- green
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