Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis-Reference-Cited by-同舟云学术

Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis

Published:2023-08-23 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Wang Huanjie,Li Yuan,Bai Xiwei,Li Jingwei,Tan Jie^ORCID,Liu Chengbao

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02186-1.pdf

Reference26 articles.

1. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1), 151–175. https://doi.org/10.1007/s10994-009-5152-4

2. Cai, T., Gao, R., Lee, J., & Lei, Q. (2021). A theory of label propagation for subpopulation shift. In International Conference on Machine Learning, PMLR (Vol. 139, pp. 1170–1182).

3. Chen, Q., Liu, Y., Wang, Z., Wassell, I., & Chetty, K. (2018). Re-weighted adversarial adaptation network for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (pp. 7976–7985). https://doi.org/10.1109/CVPR.2018.00832

4. Chen, X., Zhang, B., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987. https://doi.org/10.1007/s10845-020-01600-2

5. Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, PMLR, (Vol. 70, pp. 1126–1135).