Name
Abstract | ||
|---|---|---|
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model’s learning performance with an unlabeled (target) domain—the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target d... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNNLS.2020.3017213 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Target recognition,Task analysis,Training,Prediction algorithms,Support vector machines,Random variables,Learning systems | Journal | 32 |
Issue | ISSN | Citations |
10 | 2162-237X | 2 |
PageRank | References | Authors |
0.36 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Fang | 1 | 3 | 2.06 |
| Jie Lu | 2 | 1125 | 92.04 |
| Feng Liu | 3 | 80 | 8.59 |
| Junyu Xuan | 4 | 174 | 12.06 |
| Guangquan Zhang | 5 | 1973 | 145.64 |