Name
Title | ||
|---|---|---|
Unsupervised Domain Adaptation VIA Cluster Alignment with Maximum Classifier Discrepancy |
Abstract | ||
|---|---|---|
One way of addressing the problem of unsupervised domain adaptation (UDA) is to perform adversarial training between two classifiers and their shared feature extractor. The two classifiers are enforced to detect the misaligned regions between the source and target domains, while the feature extractor aligns the features by confusing the classifiers. Although this method yields improvement, it igno... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICME51207.2021.9428418 | 2021 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | DocType | ISBN |
Training,Resistance,Perturbation methods,Conferences,Feature extraction,Task analysis | Conference | 978-1-6654-3864-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Said Mahmoud Azzam | 1 | 1 | 1.70 |
| Si Wu | 2 | 17 | 7.03 |
| Aurele Tohokantche Gnanha | 3 | 0 | 0.34 |
| Qianfen Jiao | 4 | 1 | 1.70 |
| Hau-San Wong | 5 | 1008 | 86.89 |