Joint marginal and central sample learning for domain adaptation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11280-024-01290-3.pdf
Reference46 articles.
1. Ren, Q., Mao, Q., Lu, S.: Prototypical bidirectional adaptation and learning for cross-domain semantic segmentation. IEEE Trans. Multimed. (2023)
2. Wu, X., Wu, Z., Ju, L., Wang, S.: A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 58–72 (2021)
3. Xu, G., Huang, C., Silva, D.S., Albuquerque, V.H.C.: A compressed unsupervised deep domain adaptation model for efficient cross-domain fault diagnosis. IEEE Trans. Ind. Inform. (2022)
4. Yang, B., Lei, Y., Li, X., Roberts, C.: Deep targeted transfer learning along designable adaptation trajectory for fault diagnosis across different machines. IEEE Trans. Industr. Electron. 70(9), 9463–9473 (2022)
5. Cao, Y., Long, M., Wang, J.: Unsupervised domain adaptation with distribution matching machines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
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