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Found 3 papers, 1 papers with code
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs.
Future-Proofing Class Incremental Learning
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable.
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.
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