Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
Keywords: continual learning; incremental learning; stability-plasticity dilemma
Abstract: 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. Existing studies have proposed numerous CL methods to achieve this trade-off. However, these methods often overlook the impact of basic architecture on stability and plasticity, thus the trade-off is limited 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-Architecture (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 across datasets and CL methods demonstrate that Dual-Arch can enhance the performance of existing CL methods while being up to 87% more compact in terms of parameters than the baselines.
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Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 6928
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