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cs.LG2024
Effective and Robust Adversarial Training against Data and Label Corruptions
Peng-Fei Zhang, Zi Huang, Xin-Shun Xu +1
Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing eff…
cs.LG2022
Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation
Zixin Wang, Yadan Luo, Peng-Fei Zhang +2
A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior…