利用对称性设计特征表示，显著提升传感器活动识别的稳定性与泛化能力。
Learning with Category-Equivariant Representations for Human Activity Recognition
- 基于类别等变表示理论，构建对时间、尺度、传感器层级变化鲁棒的特征结构
- 在UCI数据集上，分布外准确率提升约46个百分点，达基线3.6倍
- 适合需要跨设备、跨环境稳定识别的智能可穿戴应用
人体活动识别面临传感器信号随上下文、运动和环境变化而漂移的挑战；有效模型必须在外界变化中保持稳定。本文提出一种考虑类别对称性的学习框架，捕捉信号在时间、尺度及传感器层级上的变化规律，并将这些因素融入特征表示结构，使模型能自动维持传感器间关系，在真实扰动如时间偏移、幅度漂移和设备朝向变化下仍保持稳定。在UCI人体活动识别基准上，该对称性驱动的设计使分布外准确率提升约46个百分点（约为基线的3.6倍），证明抽象对称性原理可通过类别等变表示理论转化为日常传感任务中的实际性能提升。
原文摘要 · Abstract (English)
Human activity recognition is challenging because sensor signals shift with context, motion, and environment; effective models must therefore remain stable as the world around them changes. We introduce a categorical symmetry-aware learning framework that captures how signals vary over time, scale, and sensor hierarchy. We build these factors into the structure of feature representations, yielding models that automatically preserve the relationships between sensors and remain stable under realistic distortions such as time shifts, amplitude drift, and device orientation changes. On the UCI Human Activity Recognition benchmark, this categorical symmetry-driven design improves out-of-distribution accuracy by approx. 46 percentage points (approx. 3.6x over the baseline), demonstrating that abstract symmetry principles can translate into concrete performance gains in everyday sensing tasks via category-equivariant representation theory.
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