33 citations · 52 across the 7 of their papers we have counts for
7 papers
Parameter Interpolation Adversarial Training for Robust Image Classification
Xin Liu, Yichen Yang, Kun He +1
Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most…
Fast Adversarial Training against Textual Adversarial Attacks
Yichen Yang, Xin Liu, Kun He
Many adversarial defense methods have been proposed to enhance the adversarial robustness of natural language processing models. However, most of them introduce additional pre-set…
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Kun He, Xin Liu, Yichen Yang +4
Adversarial training has been demonstrated to be the most effective approach to defend against adversarial attacks. However, existing adversarial training methods show apparent osc…
Robust Textual Embedding against Word-level Adversarial Attacks
Yichen Yang, Xiaosen Wang, Kun He
We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to…
Regional Adversarial Training for Better Robust Generalization
Chuanbiao Song, Yanbo Fan, Yichen Yang +4
Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks. To our knowledge, existing AT-based methods usuall…
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks
Xiaosen Wang, Yichen Yang, Yihe Deng +1
Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, exis…
Natural Language Adversarial Defense through Synonym Encoding
Xiaosen Wang, Hao Jin, Yichen Yang +1
In the area of natural language processing, deep learning models are recently known to be vulnerable to various types of adversarial perturbations, but relatively few works are don…