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.

翻译：近年来，基于提示引导的无回放方法通常在数据持续漂移的视觉相关持续学习场景中表现优异。为在实际设备上部署，这些方法必须在训练期间具备较高的资源效率。本文提出资源高效提示方法，旨在提升基于提示的无回放方法的资源效率。我们的核心目标是在精简提示学习过程中的计算与内存开销时，避免与准确率产生灾难性权衡。为此，我们通过利用快速提示选择机制来增强输入数据——该机制采用精心配置的模型实现，并针对提示更新阶段开发了自适应令牌融合算法与自适应层丢弃算法。在学习新任务时，自适应令牌融合算法与自适应层丢弃算法能在不损害任务特定特征的前提下，跨数据和模型维度执行选择性跳过。通过在三个图像分类数据集上的大量实验，我们验证了资源高效提示方法相较于当前基于视觉Transformer和卷积神经网络的最先进方法具有更优越的资源效率。