Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation-Reference-Cited by-同舟云学术

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Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation

Published:2023-12-20 Issue: Volume: Page:
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Du Yuntao,Yang Haiyang,Chen Mingcai,Luo Hongtao,Jiang Juan,Xin Yi,Wang Chongjun

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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4. Balanced Teacher for Source-Free Object Detection;IEEE Transactions on Circuits and Systems for Video Technology;2024-08

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