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arXiv · 2406.04772

REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning

Sungho Jeon·Xinyue Ma·Kwang In Kim·Myeongjae Jeon
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Abstract

Recent rehearsal-free continual learning (CL) methods guided by prompts achieve strong performance on vision tasks with non-stationary data but remain resource-intensive, hindering real-world edge deployment. We introduce resource-efficient prompting (REP), which improves the computational and memory efficiency of prompt-based rehearsal-free continual learning methods while minimizing accuracy trade-offs. Our approach employs swift prompt selection to refine input data using a carefully provisioned model and introduces adaptive token merging (AToM) and adaptive layer dropping (ALD) for efficient prompt updates. AToM and ALD selectively skip data and model layers while preserving task-specific features during the learning of new tasks. Extensive experiments on multiple image classification datasets demonstrate REP's superior resource efficiency over state-of-the-art rehearsal-free CL methods.

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Sungho Jeon, Xinyue Ma, Kwang In Kim, Myeongjae Jeon. 2024-06-07. REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning. https://arxiv.org/abs/2406.04772

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