2 citations · 5 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
DiM: -Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation
Bingli Wang, Houcheng Su, Nan Yin +2
As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation…
Degree-Conscious Spiking Graph for Cross-Domain Adaptation
Yingxu Wang, Mengzhu Wang, Houcheng Su +3
Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification by emulating brain-inspired neural dynamics to achieve energy-efficient computation. H…
Open-World Test-Time Training: Self-Training with Contrast Learning
Houcheng Su, Mengzhu Wang, Jiao Li +3
Traditional test-time training (TTT) methods, while addressing domain shifts, often assume a consistent class set, limiting their applicability in real-world scenarios characterize…
Continuous Spiking Graph Neural Networks
Nan Yin, Shiqi Fan, Mengzhu Wan +10
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing contin…
Dynamic Spiking Framework for Graph Neural Networks
Nan Yin, Mengzhu Wang, Zhenghan Chen +3
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in process…