135 citations · 164 across the 6 of their papers we have counts for
6 papers
Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning
Depeng Li, Zhigang Zeng
In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new clas…
IF2Net: Innately Forgetting-Free Networks for Continual Learning
Depeng Li, Tianqi Wang, Bingrong Xu +3
Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which inform…
Multi-View Class Incremental Learning
Depeng Li, Tianqi Wang, Junwei Chen +3
Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods mo…
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Lubin Meng, Jian Huang, Zhigang Zeng +6
Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adopti…
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
Dongrui Wu, Xue Jiang, Ruimin Peng +3
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promis…
On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression
Dongrui Wu, Chin-Teng Lin, Jian Huang +1
Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize…