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

Recent rehearsal-free methods, guided by prompts, generally excel in vision-related continual learning (CL) scenarios with continuously drifting data. To be deployable on real-world devices, these methods must contain high resource efficiency during training. In this paper, we introduce Resource-Efficient Prompting (REP), which targets improving the resource efficiency of prompt-based rehearsal-free methods. Our key focus is on avoiding catastrophic trade-offs with accuracy while trimming computational and memory costs during prompt learning. We achieve this by exploiting swift prompt selection that enhances input data using a carefully provisioned model, and by developing adaptive token merging (AToM) and layer dropping (ALD) algorithms for the prompt updating stage. AToM and ALD perform selective skipping across the data and model dimensions without compromising task-specific features while learning new tasks. We validate REP's superior resource efficiency over current state-of-the-art ViT- and CNN-based methods through extensive experiments on three image classification datasets.