19 citations · 52 across the 9 of their papers we have counts for
9 papers
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
Ran Ran, Nuo Xu, Wei Wang +3
Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal healthcare and financial…
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Yue Song, Nicu Sebe, Wei Wang
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings. In this paper, we observe that the singular value distribut…
Batch-efficient EigenDecomposition for Small and Medium Matrices
Yue Song, Nicu Sebe, Wei Wang
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particular…
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Wenju Zhang, Xiang Zhang, Qing Liao +5
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new…
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…