Exploring Robust Features for Improving Adversarial Robustness

no code implementations • 9 Sep 2023 • Hong Wang, Yuefan Deng, Shinjae Yoo, Yuewei Lin

In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i. e., invariant to the clean image and its adversarial examples, to improve the model's adversarial robustness.

Adversarial Robustness Disentanglement

AI-aided multiscale modeling of physiologically-significant blood clots

no code implementations • 25 May 2022 • Yicong Zhu, Changnian Han, Peng Zhang, Guojing Cong, James R. Kozloski, Chih-Chieh Yang, Leili Zhang, Yuefan Deng

We have developed an AI-aided multiple time stepping (AI-MTS) algorithm and multiscale modeling framework (AI-MSM) and implemented them on the Summit-like supercomputer, AIMOS.

AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning

1 code implementation • ICCV 2021 • Hong Wang, Yuefan Deng, Shinjae Yoo, Haibin Ling, Yuewei Lin

The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model.

Adversarial Attack Adversarial Robustness +2