Name
Abstract | ||
|---|---|---|
Unsupervised domain adaptation aims to learn an accurate classifier for a target domain by leveraging knowledge learned from a related (source) domain. Existing approaches focus on deriving new domain-invariant feature representations to align two domains and an extra classifier is required. In this paper, we propose a novel unsupervised domain adaptation method to train a classifier directly for ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534057 | 2021 International Joint Conference on Neural Networks (IJCNN) |
Keywords | DocType | ISSN |
Adaptation models,Benchmark testing,Graph neural networks,Iterative methods | Conference | 2161-4393 |
ISBN | Citations | PageRank |
978-1-6654-3900-8 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjie Du | 1 | 0 | 0.34 |
| Deyun Zhou | 2 | 18 | 3.49 |
| Jiao Shi | 3 | 2 | 2.11 |
| Yu Lei | 4 | 7 | 5.92 |
| Maoguo Gong | 5 | 2676 | 172.02 |