Self-adaptive label filtering learning for unsupervised domain adaptation

Graphical abstract

References

[1] Wold S , Esbensen K , Geladi P . Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1987, 2( 1−3): 37– 52

[2] Long M, Ding G, Wang J, Sun J, Guo Y, Yu P S. Transfer sparse coding for robust image representation. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. 2013, 407– 414

[3] Zhang J, Li W, Ogunbona P. Joint geometrical and statistical alignment for visual domain adaptation. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 5150– 5158

[4] Li S , Song S , Huang G , Ding Z , Wu C . Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Transactions on Image Processing, 2018, 27( 9): 4260– 4273

[5] Luo L , Chen L , Hu S , Lu Y , Wang X . Discriminative and geometry-aware unsupervised domain adaptation. IEEE Transactions on Cybernetics, 2020, 50( 9): 3914– 3927

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants Nos. 62176128 and 61702273), the Natural Science Foundation of Jiangsu Province (BK20170956), the Open Projects Program of National Laboratory of Pattern Recognition (202000007), the Fundamental Research Funds for the Central Universities (NJ2019010), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, the Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX21_1006, and was also sponsored by the Qing Lan Project.

Supporting Information

The supporting information is available online at journal. hep. com. cn and link. springer. com.

RIGHTS & PERMISSIONS

2021 Higher Education Press 2021