DASH-N: Joint Hierarchical Domain Adaptation and Feature Learning
By Hien Van Nguyen, Huy Tho Ho, Vishal M. Patel, Ramalingam Chellappa. 2015
77 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tao A. Xiang
- Ramalingam Chellappa
- Yu Qiao
- Kaiyang Zhou
- Vishal M. Patel
- Yongxin Yang
- Gabriella Csurka
- Raghuraman Gopalan
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Tao A. Xiang
- Yu Qiao
- Ramalingam Chellappa
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Trevor Darrell
Citations in the corpus
- Visual Domain Adaptation: A survey of recent advances (2015, methodology): “For deep learning approaches [77], [78], the complexity depends, among others, on the number of layers used in the hierarchy to learn feature correlation for adaptation.”
- Domain Adaptive Ensemble Learning (2020, background): “n generalization ability. To overcome the domain shift problem and improve the generalization ability of DNNs, two related problems have been studied extensively: unsupervised domain adaptation (UDA) [2, 3, 4, 5, 6, 7, 8, 9] and domain generalization (DG) [10, 11, 12, 13, 14, 15, 16]. The former ass”