9 citations · 13 across the 4 of their papers we have counts for
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cs.LG2023★ 9 cited
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…
cs.LG2022★ 1 cited
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…
cs.LG2020★ 3 cited
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…