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cs.LG2019
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Aishan Liu, Xianglong Liu, Chongzhi Zhang +3
In practice, deep neural networks have been found to be vulnerable to various types of noise, such as adversarial examples and corruption. Various adversarial defense methods have…
cs.LG2019
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks
Hang Yu, Aishan Liu, Xianglong Liu +5
Adversarial images are designed to mislead deep neural networks (DNNs), attracting great attention in recent years. Although several defense strategies achieved encouraging robustn…