Name
Abstract | ||
|---|---|---|
Existing domain adaptation approaches generalize models trained on the labeled source domain data to the unlabeled target domain data by forcing feature distributions of two domains closer. However, these approaches are likely to ignore the semantic information during the feature alignment between source and target domain. In this paper, we propose a new unsupervised domain adaptation framework to learn the cross-domain features and disentangle the semantic information concurrently. Specifically, we firstly combine the task-specific classification and domain adversarial learning to obtain the cross-domain features by mapping the data of both domains with the shared feature extractor. Secondly, we integrate the domain adversarial learning and the within-domain reconstruction to disentangle the semantic information from the domain information. Thirdly, we include a cross-domain transformation to further refine the feature extractor, which in turn improves the performances of the task classifier. We compare our proposed model to previous state-of-the-art methods on domain adaptation digit classification tasks. Experimental results show that our model achieves better performances than the other counterparts, which demonstrates the superiority and effectiveness of our model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545300 | 2018 24th International Conference on Pattern Recognition (ICPR) |
Keywords | Field | DocType |
Unsupervised domain adaptation,deep neural network,adversarial learning | Task analysis,Pattern recognition,Domain adaptation,Computer science,Feature extraction,Extractor,Artificial intelligence,Semantic feature,Classifier (linguistics),Semantics,Adversarial system | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-5386-3789-0 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Li | 1 | 209 | 59.97 |
| Wen-Ming Cao | 2 | 26 | 11.53 |
| Sheng Qian | 3 | 19 | 4.02 |
| Hau-San Wong | 4 | 1008 | 86.89 |
| Si Wu | 5 | 17 | 7.03 |