no code implementations • 26 Mar 2022 • Zhuang Qian, Kaizhu Huang, Qiu-Feng Wang, Xu-Yao Zhang
In this paper, we present a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition.
no code implementations • 29 Sep 2021 • Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Bin Gu, Huan Xiong, Xinping Yi
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
1 code implementation • 8 Jul 2021 • Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
no code implementations • ICLR 2020 • Shufei Zhang, Zhuang Qian, Kai-Zhu Huang, Jimin Xiao, Yuan He
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.
no code implementations • 18 Jan 2019 • Zhuang Qian, Kai-Zhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang
Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution.