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Found 4 papers, 3 papers with code
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks
The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks.
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
The high cost of data labeling often results in node label shortage in real applications.
Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis.
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