19 citations · 31 across the 4 of their papers we have counts for
5 papers
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?
Yue Song, Nicu Sebe, Wei Wang
Global covariance pooling (GCP) aims at exploiting the second-order statistics of the convolutional feature. Its effectiveness has been demonstrated in boosting the classification…
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Baopu Li, Shuhui Yang +6
Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerni…
Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy
Mengzhu Wang, Xiang Zhang, Long Lan +3
Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain ada…
Rethink Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Haojie Li, Zhengming Ding +1
Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by estab…
Sparsely-Labeled Source Assisted Domain Adaptation
Wei Wang, Zhihui Wang, Yuankai Xiang +4
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available…