<ul><li>The quest for Continual Learning (CL) aims to enable neural networks to learn and adapt incrementally.</li><li>Addressing the stability-plasticity dilemma in CL involves balancing preserving prior knowledge with acquiring new knowledge.</li><li>A new framework called Dual-Arch is introduced to address the conflict between stability and plasticity at the architectural level.</li><li>Dual-Arch utilizes two independent networks, each specialized for plasticity and stability, leading to improved performance in CL methods with reduced parameters.</li></ul>

Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective

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