3 citations · 9 across the 15 of their papers we have counts for
4 papers · 1 filter
Rethinking Adversarial Training with Neural Tangent Kernel
Guanlin Li, Han Qiu, Shangwei Guo +2
Adversarial training (AT) is an important and attractive topic in deep learning security, exhibiting mysteries and odd properties. Recent studies of neural network training dynamic…
Alleviating the Effect of Data Imbalance on Adversarial Training
Guanlin Li, Guowen Xu, Tianwei Zhang
In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. Compared with conventiona…
Omnipotent Adversarial Training in the Wild
Guanlin Li, Kangjie Chen, Yuan Xu +2
Adversarial training is an important topic in robust deep learning, but the community lacks attention to its practical usage. In this paper, we aim to resolve a real-world challeng…
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Xiaoxuan Lou, Guowen Xu, Kangjie Chen +3
Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight b…