Abstract: When distribution shifts occur between testing and training graph data, out-of-distribution (OOD) samples undermine the performance of graph neural networks (GNNs). To improve adaptive OOD ...
Abstract: Traditional cross-domain tasks, including unsupervised domain adaptation (UDA), domain generalization (DG) and test-time adaptation (TTA), rely heavily on the training model by source domain ...
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