← 研踪 PaperTrailCVF · CVPR 2026:cvf:d6de6bca1014d59b08ed15aa3257f0da · 记录 #1
Spectral Mixture-of-Experts for Continual Learning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:03
摘要
While Parameter-Efficient Fine-Tuning using Mixture-of-Experts (MoE) is a promising solution for continual learning (CL), it suffers from two critical failure modes: structural interference, where expert updates interfere, and compositional forgetting, where the model's routing policy drifts. To address these issues, we introduce Spectral MoE, a novel framework built for CL from three core components. First, Spectral Experts are parameterized using unique, disjoint spectral masks to confine their learnable parameters to distinct frequency subspaces, ensuring a priori orthogonal updates that prevent structural interference. Second, a Dual-Router mechanism decouples online routing that learns new tasks from an offline memory that archives historical expert importance. Finally, this offline memory enables a Dynamic Consistency Projection, a geometric constraint that suppresses router drift and adaptively shields experts based on their past contributions, mitigating compositional forgetting. Validated on a strict cross-domain CL benchmark, our framework significantly outperforms existing methods, demonstrating superior knowledge retention and plasticity for new tasks. Code is available at: https://github.com/ouycc/Spectral_MoE.
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记录 #1 · 2026 · 最新观测版本
Spectral Mixture-of-Experts for Continual Learning
While Parameter-Efficient Fine-Tuning using Mixture-of-Experts (MoE) is a promising solution for continual learning (CL), it suffers from two critical failure modes: structural interference, where expert updates interfere, and compositional forgetting, where the model's routing policy drifts. To address these issues, we introduce Spectral MoE, a novel framework built for CL from three core components. First, Spectral Experts are parameterized using unique, disjoint spectral masks to confine their learnable parameters to distinct frequency subspaces, ensuring a priori orthogonal updates that prevent structural interference. Second, a Dual-Router mechanism decouples online routing that learns new tasks from an offline memory that archives historical expert importance. Finally, this offline memory enables a Dynamic Consistency Projection, a geometric constraint that suppresses router drift and adaptively shields experts based on their past contributions, mitigating compositional forgetting. Validated on a strict cross-domain CL benchmark, our framework significantly outperforms existing methods, demonstrating superior knowledge retention and plasticity for new tasks. Code is available at: https://github.com/ouycc/Spectral_MoE.
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