19 citations · 25 across the 6 of their papers we have counts for
6 papers
On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning
Tianqi Wang, Jingcai Guo, Depeng Li +1
Exemplar-free class incremental learning (EF-CIL) is a nontrivial task that requires continuously enriching model capability with new classes while maintaining previously learned k…
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
Depeng Li, Tianqi Wang, Junwei Chen +2
Class-incremental learning (CIL) aims to train a model to learn new classes from non-stationary data streams without forgetting old ones. In this paper, we propose a new kind of co…
Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding
Depeng Li, Tianqi Wang, Junwei Chen +3
Deep neural networks are susceptible to catastrophic forgetting when trained on sequential tasks. Various continual learning (CL) methods often rely on exemplar buffers or/and netw…
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…