30 citations · 72 across the 10 of their papers we have counts for
10 papers
Vector Quantization Prompting for Continual Learning
Li Jiao, Qiuxia Lai, Yu Li +1
Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that…
On Function-Coupled Watermarks for Deep Neural Networks
Xiangyu Wen, Yu Li, Wei Jiang +1
Well-performed deep neural networks (DNNs) generally require massive labelled data and computational resources for training. Various watermarking techniques are proposed to protect…
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification
Yu Li, Muxi Chen, Yannan Liu +2
Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on diffe…
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
Yijun Yang, Ruiyuan Gao, Yu Li +2
Adversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains, e.g., autonomous driving. While there has been a vast…
Information Bottleneck Approach to Spatial Attention Learning
Qiuxia Lai, Yu Li, Ailing Zeng +3
The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near…
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
Yu Li, Min Li, Qiuxia Lai +2
Deep learning (DL) has achieved unprecedented success in a variety of tasks. However, DL systems are notoriously difficult to test and debug due to the lack of explainability of DL…
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
Min Li, Yu Li, Ye Tian +2
This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input,…
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis
Yijun Yang, Ruiyuan Gao, Yu Li +2
Machine learning with deep neural networks (DNNs) has become one of the foundation techniques in many safety-critical systems, such as autonomous vehicles and medical diagnosis sys…
DeepDyve: Dynamic Verification for Deep Neural Networks
Yu Li, Min Li, Bo Luo +2
Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, how…
On Configurable Defense against Adversarial Example Attacks
Bo Luo, Min Li, Yu Li +1
Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications. Recently, however, DNNs are shown to be vulnerable to adversaria…