1 code implementation • 7 May 2021 • Bangjie Yin, Wenxuan Wang, Taiping Yao, Junfeng Guo, Zelun Kong, Shouhong Ding, Jilin Li, Cong Liu
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.
no code implementations • CVPR 2020 • Zelun Kong, Junfeng Guo, Ang Li, Cong Liu
We compare PhysGAN with a set of state-of-the-art baseline methods including several of our self-designed ones, which further demonstrate the robustness and efficacy of our approach.
no code implementations • 13 Nov 2018 • Zhuoyi Wang, Zelun Kong, Hemeng Tao, Swarup Chandra, Latifur Khan
In this paper, we address this key challenge by proposing a semi-supervised multi-task learning framework called \sysname{} which aims to intrinsically search for a latent space suitable for detecting labels of instances from both known and unknown classes.