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cs.LG2021★ 1 cited
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
cs.LG2020
TEAM: We Need More Powerful Adversarial Examples for DNNs
Yaguan Qian, Ximin Zhang, Bin Wang +4
Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassificati…