79 citations · 155 across the 5 of their papers we have counts for
5 papers
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, Tianle Chen, Surya Nepal +3
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and…
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Shuo Wang, Tianle Chen, Shangyu Chen +3
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as…
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
Shuo Wang, Surya Nepal, Carsten Rudolph +3
Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teac…
Generating Semantic Adversarial Examples via Feature Manipulation
Shuo Wang, Surya Nepal, Carsten Rudolph +3
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wi…
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Xin Dong, Shangyu Chen, Sinno Jialin Pan
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along…