109 citations · 146 across the 5 of their papers we have counts for
5 papers
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
Wenqi Shao, Hang Yu, Zhaoyang Zhang +3
This work presents a probabilistic channel pruning method to accelerate Convolutional Neural Networks (CNNs). Previous pruning methods often zero out unimportant channels in traini…
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
Aishan Liu, Jiakai Wang, Xianglong Liu +3
Adversarial examples are inputs with imperceptible perturbations that easily misleading deep neural networks(DNNs). Recently, adversarial patch, with noise confined to a small and…
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…
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
Chongzhi Zhang, Aishan Liu, Xianglong Liu +4
Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Despite the potential risk they…
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…