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Found 6 papers, 0 papers with code
Dynamic Spiking Graph Neural Networks
Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation.
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation
Recent domain adaptation study has shown that maximizing the sum of singular values of prediction results can simultaneously enhance their confidence (discriminability) and diversity.
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Further, we provide an in-depth analysis of the mechanism and rational behind our approach, which gives us a better understanding of why leverage logits in lieu of features can help domain generalization.
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.
Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy
In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way.
