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cs.LG2024
Mitigating Label Noise on Graph via Topological Sample Selection
Yuhao Wu, Jiangchao Yao, Xiaobo Xia +4
Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world g…
cs.LG2024
Understanding Robust Overfitting from the Feature Generalization Perspective
Chaojian Yu, Xiaolong Shi, Jun Yu +2
Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. However, it is plagued by the issue of robust overfitting…
cs.LG2024
Tackling Noisy Labels with Network Parameter Additive Decomposition
Jingyi Wang, Xiaobo Xia, Long Lan +5
Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows…