Name
Abstract | ||
|---|---|---|
Adversarial domain adaptation is a powerful approach to transfer the knowledge of the label-rich source domain to the label-scarce target domain by mitigating domain shifts across distributions. Existing domain adaptation methods align either the marginal distribution with a single-domain discriminator or conditional distributions with multiple-domain discriminators. However, aligning both marginal (global) and conditional (local) distributions should be considered for domain adaptation. This paper proposes a novel adversarial distribution adaptation network (ADAN) to jointly reduce both the global and local distribution discrepancies between different domains for learning domain-invariant representations. ADAN utilizes a single-domain discriminator to adapt the global distribution between two domains, and source decision boundaries to align the local distributions between sub-domains. Furthermore, we extend our ADAN as improved ADAN (iADAN), in which we utilize a feature norm term to regularize the task-specific features to improve model generalization. Extensive experimental results show that our method outperforms other state-of-the-art domain adaptation methods on Office-Home and ImageCLEF-DA datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00521-020-05513-2 | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
Domain adaptation, Adversarial training, Image classification | Journal | 33 |
Issue | ISSN | Citations |
13 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Zhou | 1 | 6 | 3.50 |
| Wenan Zhou | 2 | 50 | 19.20 |
| Shirui Wang | 3 | 3 | 1.75 |
| Ying Xing | 4 | 5 | 3.87 |