1 citations · 2 across the 6 of their papers we have counts for
8 papers
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Jiaxin Huang +4
Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. However, existing methods are largely f…
USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
Yingxu Wang, Kunyu Zhang, Mengzhu Wang +2
SF-GDA is pivotal for privacy-preserving knowledge transfer across graph datasets. Although recent works incorporate structural information, they implicitly condition adaptation on…
FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection
Mengzhu Wang, Changyuan Deng, Shanshan Wang +3
Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent metho…
Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
Yingxu Wang, Mengzhu Wang, Zhichao Huang +2
Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential…
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
Houcheng Su, Mengzhu Wang, Jiao Li +3
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its…
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation
Mengzhu Wang, Jiao Li, Houcheng Su +3
Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing da…