50 citations · 64 across the 6 of their papers we have counts for
6 papers
Hierarchical Memory Pool Based Edge Semi-Supervised Continual Learning Method
Xiangwei Wang, Rui Han, Chi Harold Liu
The continuous changes in the world have resulted in the performance regression of neural networks. Therefore, continual learning (CL) area gradually attracts the attention of more…
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Yaxin Luopan, Rui Han, Qinglong Zhang +2
Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real w…
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision
Rui Han, Qinglong Zhang, Chi Harold Liu +3
Deep neural networks (DNNs) have become ubiquitous techniques in mobile and embedded systems for applications such as image/object recognition and classification. The trend of exec…
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Amirmasoud Ghiassi, Robert Birke, Rui Han +1
Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant por…
RAD: On-line Anomaly Detection for Highly Unreliable Data
Zilong Zhao, Robert Birke, Rui Han +4
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source…