Improving Adversarial Robustness via Guided Complement Entropy

ICCV 2019 Hao-Yun ChenJhao-Hong LiangShih-Chieh ChangJia-Yu PanYu-Ting ChenWei WeiDa-Cheng Juan

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent methods have proposed to improve adversarial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training... (read more)

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