Self-adaptive label filtering learning for unsupervised domain adaptation

Qing TIAN; Heyang SUN; Shun PENG; Tinghuai MA

doi:10.1007/s11704-022-1283-6

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (1) : 171308 DOI: 10.1007/s11704-022-1283-6

Artificial Intelligence

LETTER

Self-adaptive label filtering learning for unsupervised domain adaptation

Qing TIAN ¹^,²^,^†
, Heyang SUN ¹
, Shun PENG ¹
, Tinghuai MA ¹^,²

Author information +

History +

PDF (3924KB)

Graphical abstract

Cite this article

Download citation ▾

Qing TIAN, Heyang SUN, Shun PENG, Tinghuai MA. Self-adaptive label filtering learning for unsupervised domain adaptation. Front. Comput. Sci., 2023, 17(1): 171308 DOI:10.1007/s11704-022-1283-6

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[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