In this paper, we propose Neighborhood-based Credibility Anchor Learning (NCAL), a new threshold-free framework that fully mines the neighborhood structure ...
Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain in the presence of distribution shift and class ...
In this chapter, we discuss the main trends that have emerged for domain adaptation in the deep learning realm, and, when appropriate, relate them to the ...
Neighborhood-based credibility anchor learning for universal domain adaptation ... Authors: Wan Su; Zhongyi Han; Rundong He; Benzheng Wei; Xueying He; Yilong Yin ...
Feb 28, 2024 · Neighborhood-based credibility anchor learning for universal domain adaptation. Pattern Recognition, 142: 109686. Tranheden et al. (2021)
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2023. Neighborhood-based credibility anchor learning for universal domain adaptation. W Su, Z Han, R He, B Wei, X He, Y Yin. Pattern Recognition 142, 109686 ...
Neighborhood-based credibility anchor learning for univer- sal domain adaptation. Pattern Recognition, 142: 109686. Tranheden, W.; Olsson, V.; Pinto, J ...
This paper proposes a Hierarchical Feature Disentangling Network (HFDN) to disentangle domain-relevant features into domain-specific and category-shift ...
Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain without any ...
Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any ...
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