most citedImproving Generalization of Adversarial Training via Robust Critical Fine-Tuning

2 citations · 3 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2023★ 2 cited

Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning

Kaijie Zhu, Jindong Wang, Xixu Hu +2

Deep neural networks are susceptible to adversarial examples, posing a significant security risk in critical applications. Adversarial Training (AT) is a well-established technique…

cs.LG2023

Frustratingly Easy Model Generalization by Dummy Risk Minimization

Juncheng Wang, Jindong Wang, Xixu Hu +2

Empirical risk minimization (ERM) is a fundamental machine learning paradigm. However, its generalization ability is limited in various tasks. In this paper, we devise Dummy Risk M…

cs.LG2023★ 1 cited

Deep into The Domain Shift: Transfer Learning through Dependence Regularization

Shumin Ma, Zhiri Yuan, Qi Wu +5

Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in…