RainbowPrompt：面向持续学习的多样性增强提示演化方法
RainbowPrompt: Diversity-Enhanced Prompt-Evolving for Continual Learning
AI总结：
针对现有基于提示的持续学习方法表示多样性不足的问题，提出RainbowPrompt提示演化机制，通过变换对齐基础提示、引入可学习概率门控实现知识持续演化，在图像分类和视频动作识别任务上取得显著性能提升。
摘要：
基于提示的持续学习通过调优小部分参数、同时冻结预训练模型，提供了一种无需排练的解决方案。为满足序列任务的复杂需求，在提示中有效整合任务专属知识至关重要。然而现有工作要么依赖固定的已学习提示（即其表示在新任务学习期间保持不变的提示），要么依赖从纠缠的任务共享空间生成的提示，限制了整合后提示的表示多样性。为解决该问题，我们提出一种新颖的提示演化机制，可自适应地将基础提示（即任务专属提示）聚合为统一提示，同时保证多样性。通过对已学习和新引入的基础提示进行变换与对齐，我们的方法能持续演化累积的知识以促进新任务学习。我们进一步引入可学习概率门控，可在演化过程中自适应决定激活哪些层。我们在类别增量学习的图像分类和视频动作识别任务上验证了方法，在所有场景下较现有方法分别取得9.07%和7.40%的平均提升。
英文摘要：
Prompt-based continual learning provides a rehearsal-free solution by tuning small sets of parameters while keeping pre-trained models frozen. To meet the complex demands of sequential tasks, it is crucial to integrate task-specific knowledge within prompts effectively. However, existing works rely on either fixed learned prompts (i.e., prompts whose representations remain unchanged during new task learning) or on prompts generated from an entangled task-shared space, limiting the representational diversity of the integrated prompt. To address this issue, we propose a novel prompt-evolving mechanism to adaptively aggregate base prompts (i.e., task-specific prompts) into a unified prompt while ensuring diversity. By transforming and aligning base prompts, both previously learned and newly introduced, our approach continuously evolves accumulated knowledge to facilitate learning new tasks. We further introduce a learnable probabilistic gate that adaptively determines which layers to activate during the evolution process. We validate our method on image classification and video action recognition tasks in class-incremental learning, achieving average gains of 9.07% and 7.40% over existing methods across all scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
