Name
Title | ||
|---|---|---|
Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks |
Abstract | ||
|---|---|---|
Deep learning is a powerful tool for domain adaptation by learning robust high-level domain invariant representations. Recently, adversarial domain adaptation models are applied to learn representations with adversarial training manners in feature space. However, existing models often ignore the generation process for domain adaptation. To tackle this problem, deep cycle autoencoder (DCA) is propo... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-cvi.2019.0304 | IET Computer Vision |
Keywords | Field | DocType |
image classification,image representation,learning (artificial intelligence),unsupervised learning | Feature vector,Autoencoder,Discriminator,Pattern recognition,Invariant (mathematics),Encoder,Artificial intelligence,Deep learning,Linear classifier,Classifier (linguistics),Mathematics | Journal |
Volume | Issue | ISSN |
13 | 7 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Zhou | 1 | 6 | 3.50 |
| Wenan Zhou | 2 | 50 | 19.20 |
| Bin Yang | 3 | 41 | 6.26 |
| Jun Huan | 4 | 1211 | 81.09 |