元认知选择性集成用于移动系统
Metacognitive Selective Ensemble for Mobile Systems
- Yonsei University（延世大学）
- Dankook University（檀国大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出MetaSE主动集成框架，利用可靠性短期持续性维护小型活跃集，在移动感知中实现高效推理，性能优于固定集成且接近全集成，速度提升2.7倍。
AI中文摘要：
深度集成提高了移动感知的鲁棒性，但在连续传感器流上反复执行多个模型成本高昂。仅选择少数成员可降低此成本，然而自适应选择通常需要额外的模型执行来获取关于非活跃候选者的可靠证据。我们提出MetaSE，一种主动集成框架，利用每个模型可靠性的短期持续性。MetaSE在窗口间维护一个小型活跃集，利用执行后证据拒绝不可靠成员，并仅在需要替换时调用轻量级路由。这种有状态设计无需重复全池评估即可访问更大池的多样性。在四个HAR数据集和四种模型架构上，MetaSE持续优于固定三模型集成，并达到与显著更昂贵的自适应和全集成推理相当的精度。在Raspberry Pi 4B上，MetaSE比全十模型推理快2.7倍，内存使用减少69%。
英文摘要：
Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few members reduces this cost, yet adaptive selection often requires additional model execution to obtain reliable evidence about inactive candidates. We present MetaSE, an active ensemble framework that exploits short-term persistence in per-model reliability. MetaSE maintains a small active set across windows, uses post-execution evidence to reject unreliable members, and invokes lightweight routing only when replacement is needed. This stateful design accesses the diversity of a larger pool without repeated full-pool evaluation. Across four HAR datasets and four model architectures, MetaSE consistently improves over a fixed three-model ensemble and achieves accuracy comparable to substantially more expensive adaptive and full-ensemble inference. On a Raspberry Pi 4B, MetaSE is 2.7x faster and uses 69% less memory than full ten-model inference.
