基于混合专家的统一类别和领域增量学习用于室内定位
Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization
AI总结：
MOELO提出了一种基于混合专家的统一持续学习框架，用于解决室内定位中的领域和类别增量学习问题，显著提升了定位精度和适应性。
摘要：
室内定位使用机器学习因其对基于位置的服务需求增长而受到关注。然而，其长期可靠性受到移动设备硬件/软件差异的阻碍，这些差异会改变模型的输入分布，造成领域偏移。此外，不断变化的室内环境可能会随着时间的推移引入新位置，扩大输出空间，造成类别偏移，使静态机器学习模型在长期内无效。为了解决这些挑战，我们提出了一种新的统一持续学习框架，用于室内定位，称为MOELO，首次同时解决了领域增量和类别增量学习场景。MOELO使轻量、稳健和适应性的定位解决方案成为可能，可以在资源有限的移动设备上部署，并在动态、异构的真实世界环境中实现持续学习。这得益于混合专家架构，其中专家是按区域逐步训练的，并通过基于等角紧框架的门控机制进行选择，确保了高效的路由、低延迟推理，所有这些都在紧凑的模型足迹内完成。实验评估显示，MOELO在多样化的建筑、移动设备和学习场景中，平均定位误差改进达25.6倍，最坏情况定位误差改进达44.5倍，遗忘减少21.5倍，优于最先进的框架。
英文摘要：
Indoor localization using machine learning has gained traction due to the growing demand for location-based services. However, its long-term reliability is hindered by hardware/software variations across mobile devices, which shift the model's input distribution to create domain shifts. Further, evolving indoor environments can introduce new locations over time, expanding the output space to create class shifts, making static machine learning models ineffective over time. To address these challenges, we propose a novel unified continual learning framework for indoor localization called MOELO that, for the first time, jointly addresses domain-incremental and class-incremental learning scenarios. MOELO enables a lightweight, robust, and adaptive localization solution that can be deployed on resource-limited mobile devices and is capable of continual learning in dynamic, heterogeneous real-world settings. This is made possible by a mixture-of-experts architecture, where experts are incrementally trained per region and selected through an equiangular tight frame based gating mechanism ensuring efficient routing, and low-latency inference, all within a compact model footprint. Experimental evaluations show that MOELO achieves improvements of up to 25.6x in mean localization error, 44.5x in worst-case localization error, and 21.5x lesser forgetting compared to state-of-the-art frameworks across diverse buildings, mobile devices, and learning 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。
