Rethinking the Stability-Plasticity Trade-off in Continual Learning from
  an Architectural Perspective
Authors: name: Aojun Lu, name: Hangjie Yuan, name: Tao Feng, name: Yanan Sun

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-plasticity dilemma, which involves striking a balance
between two conflicting objectives: preserving previously learned knowledge and
acquiring new knowledge. While numerous CL methods aim to achieve this
trade-off, they often overlook the impact of network architecture on stability
and plasticity, restricting the trade-off to the parameter level. In this
paper, we delve into the conflict between stability and plasticity at the
architectural level. We reveal that under an equal parameter constraint, deeper
networks exhibit better plasticity, while wider networks are characterized by
superior stability. To address this architectural-level dilemma, we introduce a
novel framework denoted Dual-Arch, which serves as a plug-in component for CL.
This framework leverages the complementary strengths of two distinct and
independent networks: one dedicated to plasticity and the other to stability.
Each network is designed with a specialized and lightweight architecture,
tailored to its respective objective. Extensive experiments demonstrate that
Dual-Arch enhances the performance of existing CL methods while being up to 87%
more compact in terms of parameters.