5 papers
Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning
Congren Dai, Huichi Zhou, Jiahao Huang +5
Online Continual Learning (OCL) involves sequentially arriving data and is particularly challenged by catastrophic forgetting, which significantly impairs model performance. To add…
Self-Controlled Dynamic Expansion Model for Continual Learning
Runqing Wu, Kaihui Huang, Hanyi Zhang +1
Continual Learning (CL) epitomizes an advanced training paradigm wherein prior data samples remain inaccessible during the acquisition of new tasks. Numerous investigations have de…
Information-Theoretic Dual Memory System for Continual Learning
RunQing Wu, KaiHui Huang, HanYi Zhang +4
Continuously acquiring new knowledge from a dynamic environment is a fundamental capability for animals, facilitating their survival and ability to address various challenges. This…
Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning
KaiHui Huang, RunQing Wu, JinHui Sheng +4
Continual learning has emerged as a pivotal area of research, primarily due to its advantageous characteristic that allows models to persistently acquire and retain information. Ho…
Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model
Runqing Wu, Fei Ye, Qihe Liu +3
Continual Learning seeks to develop a model capable of incrementally assimilating new information while retaining prior knowledge. However, current research predominantly addresses…