Name
Abstract | ||
|---|---|---|
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial train... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPAMI.2020.2991050 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Measurement,Training,Kernel,Task analysis,Adaptation models,Benchmark testing,Games | Journal | 43 |
Issue | ISSN | Citations |
11 | 0162-8828 | 8 |
PageRank | References | Authors |
0.47 | 34 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Li | 1 | 597 | 44.26 |
| Erpeng Chen | 2 | 8 | 0.47 |
| Zhengming Ding | 3 | 536 | 39.14 |
| Lei Zhu | 4 | 854 | 51.69 |
| Ke Lu | 5 | 64 | 4.71 |
| Heng Tao Shen | 6 | 6020 | 267.19 |