FlyPrompt: 基于大脑启发的随机扩展路由与时间-集成专家的通用持续学习
FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
AI总结：
FlyPrompt通过分解通用持续学习为专家路由和专家能力提升两个子问题，提出了一种脑启发框架，利用随机扩展分析路由器和时间集成输出头，提升持续学习性能，实验显示在CIFAR-100、ImageNet-R和CUB-200上分别取得11.23%、12.43%和7.62%的提升。
摘要：
通用持续学习（GCL）挑战智能系统在无明确任务边界的情况下从单次通过、非平稳数据流中学习。尽管最近在预训练模型持续参数高效调优（PET）方面的进展颇具前景，但它们通常依赖多个训练轮次和显式任务提示，限制了其在GCL场景中的有效性。此外，现有方法往往缺乏针对性设计，未能解决持续PET中的两个根本挑战：如何将专家参数分配给演变的数据分布，以及如何在有限监督下提升其表征能力。受果蝇分层记忆系统的特点（稀疏扩展和模块化集成）启发，我们提出了FlyPrompt，一种脑启发框架，将GCL分解为两个子问题：专家路由和专家能力提升。FlyPrompt引入了随机扩展分析路由器用于实例级专家激活，以及时间集成的输出头以动态适应决策边界。广泛的理论和经验评估证明了FlyPrompt的优越性能，在CIFAR-100、ImageNet-R和CUB-200上分别达到11.23%、12.43%和7.62%的提升。我们的源代码可在https://github.com/AnAppleCore/FlyGCL获取。
英文摘要：
General continual learning (GCL) challenges intelligent systems to learn from single-pass, non-stationary data streams without clear task boundaries. While recent advances in continual parameter-efficient tuning (PET) of pretrained models show promise, they typically rely on multiple training epochs and explicit task cues, limiting their effectiveness in GCL scenarios. Moreover, existing methods often lack targeted design and fail to address two fundamental challenges in continual PET: how to allocate expert parameters to evolving data distributions, and how to improve their representational capacity under limited supervision. Inspired by the fruit fly's hierarchical memory system characterized by sparse expansion and modular ensembles, we propose FlyPrompt, a brain-inspired framework that decomposes GCL into two subproblems: expert routing and expert competence improvement. FlyPrompt introduces a randomly expanded analytic router for instance-level expert activation and a temporal ensemble of output heads to dynamically adapt decision boundaries over time. Extensive theoretical and empirical evaluations demonstrate FlyPrompt's superior performance, achieving up to 11.23%, 12.43%, and 7.62% gains over state-of-the-art baselines on CIFAR-100, ImageNet-R, and CUB-200, respectively. Our source code is available at https://github.com/AnAppleCore/FlyGCL.
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。
