R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning

TL;DR

R^2MoE introduces a parameter-efficient mixture-of-experts framework that effectively mitigates catastrophic forgetting and reduces parameter growth in lifelong visual concept learning, enabling high-fidelity image generation.

Contribution

It presents a novel redundancy-removal mixture of experts framework with routing distillation and hierarchical attention, advancing continual learning with minimal parameter overhead.

Findings

Achieves 87.8% reduction in forgetting rates.

Reduces expert parameters by 63.3%.

Outperforms state-of-the-art in conceptual fidelity.

Abstract

Enabling large-scale generative models to continuously learn new visual concepts is essential for personalizing pre-trained models to meet individual user preferences. Existing approaches for continual visual concept learning are constrained by two fundamental challenges: catastrophic forgetting and parameter expansion. In this paper, we propose Redundancy-Removal Mixture of Experts (R^2MoE), a parameter-efficient framework for lifelong visual concept learning that effectively learns new concepts while incurring minimal parameter overhead. Our framework includes three key innovative contributions: First, we propose a mixture-of-experts framework with a routing distillation mechanism that enables experts to acquire concept-specific knowledge while preserving the gating network's routing capability, thereby effectively mitigating catastrophic forgetting. Second, we propose a strategy for eliminating redundant layer-wise experts that reduces the number of expert parameters by fully utilizing previously learned experts. Third, we employ a hierarchical local attention-guided inference approach to mitigate interference between generated visual concepts. Extensive experiments have demonstrated that our method generates images with superior conceptual fidelity compared to the state-of-the-art (SOTA) method, achieving an impressive 87.8\% reduction in forgetting rates and 63.3\% fewer parameters on the CustomConcept 101 dataset. Our code is available at {https://github.com/learninginvision/R2MoE}