Author:
Tian Liang,Zhou Lihua,Zhang Hao,Wang Zhenbin,Ye Mao
Funder
National Natural Science Foundation of China
Sichuan Province Science and Technology Support Program
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Reference34 articles.
1. Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3723–3732 (2018)
2. Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
3. Liang, J., Hu, D., Feng, J.: Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In: International Conference on Machine Learning, pp. 6028–6039. PMLR (2020)
4. Li, R., Jiao, Q., Cao, W., Wong, H.-S., Wu, S.: Model adaptation: unsupervised domain adaptation without source data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9641–9650 (2020)
5. Qiu, Z., Zhang, Y., Lin, H., Niu, S., Liu, Y., Du, Q., Tan, M.: Source-free domain adaptation via avatar prototype generation and adaptation. arXiv preprint arXiv:2106.15326 (2021)
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