9 citations · 13 across the 4 of their papers we have counts for
4 papers
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Nan Yin, Li Shen, Mengzhu Wang +5
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costl…
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Mengzhu Wang, Jianlong Yuan, Qi Qian +2
Domain generalization (DG) aims to learn a model on one or more different but related source domains that could be generalized into an unseen target domain. Existing DG methods try…
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…
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…