Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-023-06432-8.pdf
Reference62 articles.
1. Ahmed, S. M., Raychaudhuri, D. S., Paul, S., Oymak, S., & Roy-Chowdhury, A. (2021). Unsupervised multi-source domain adaptation without access to source data. In CVPR.
2. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F. C., & Vaughan, J. W. (2009). A theory of learning from different domains. Machine Learning, 79, 151–175.
3. Chen, Q., Du, Y., Tan, Z., Zhang, Y., & Wang, C.-J. (2020). Unsupervised domain adaptation with joint domain-adversarial reconstruction networks. In ECML/PKDD.
4. Chen, C., Fu, Z., Chen, Z., Jin, S., Cheng, Z., Jin, X., & Hua, X. (2020). Homm: Higher-order moment matching for unsupervised domain adaptation. In AAAI.
5. Cicek, S., & Soatto, S. (2019). Unsupervised domain adaptation via regularized conditional alignment. In 2019 IEEE/CVF international conference on computer vision (ICCV), pp. 1416–1425.
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