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C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning
Wei Li, Hangjie Yuan, Zixiang Zhao +4
Balancing sensitivity to new tasks and stability for retaining past knowledge is crucial in continual learning (CL). Recently, sharpness-aware minimization has proven effective in…
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
Aojun Lu, Hangjie Yuan, Tao Feng +1
The quest for Continual Learning (CL) seeks to empower neural networks with the ability to learn and adapt incrementally. Central to this pursuit is addressing the stability-plasti…
Adapt before Continual Learning
Aojun Lu, Tao Feng, Hangjie Yuan +2
Continual Learning (CL) seeks to enable neural networks to incrementally acquire new knowledge (plasticity) while retaining existing knowledge (stability). Although pre-trained mod…
Achieving Deep Continual Learning via Evolution
Aojun Lu, Junchao Ke, Chunhui Ding +3
Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance…