most citedGradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization

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

collaborators

2 papers

cs.LG2021

Bridged Adversarial Training

Hoki Kim, Woojin Lee, Sungyoon Lee +1

Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different…

cs.LG2021★ 2 cited

GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization

Sungyoon Lee, Hoki Kim, Jaewook Lee

Deep learning is vulnerable to adversarial examples. Many defenses based on randomized neural networks have been proposed to solve the problem, but fail to achieve robustness again…