Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the videos themselves can be effectively used to label objects or events, wearable data do not contain obvious cues about the physical manifestation of the users and usually require rich metadata. As a result, label noise can become an increasingly thorny issue when labeling such data. In this paper, we propose a novel solution to address noisy label learning, entitled Few-Shot Human-in-the-Loop Refinement (FHLR). Our method initially learns a seed model using weak labels. Next, it fine-tunes the seed model using a handful of expert corrections. Finally, it achieves better generalizability and robustness by merging the seed and fine-tuned models via weighted parameter averaging. We evaluate our approach on four challenging tasks and datasets, and compare it against eight competitive baselines designed to deal with noisy labels. We show that FHLR achieves significantly better performance when learning from noisy labels and achieves state-of-the-art by a large margin, with up to 19% accuracy improvement under symmetric and asymmetric noise. Notably, we find that FHLR is particularly robust to increased label noise, unlike prior works that suffer from severe performance degradation. Our work not only achieves better generalization in high-stakes health sensing benchmarks but also sheds light on how noise affects commonly-used models.

翻译：可穿戴技术能够持续监测多种健康指标，如身体活动、心率、睡眠和压力水平。可穿戴数据面临的一个关键挑战是获取高质量标签。与视频等模态不同（视频本身可有效用于标记物体或事件），可穿戴数据缺乏关于用户物理表现的明显线索，通常需要丰富的元数据。因此，在标记此类数据时，标签噪声可能成为日益棘手的问题。本文提出了一种解决噪声标签学习的新方法，称为少样本人在回路优化（FHLR）。该方法首先利用弱标签学习种子模型，接着通过少量专家修正对种子模型进行微调，最后通过加权参数平均融合种子模型与微调后的模型，实现更好的泛化性和鲁棒性。我们在四个具有挑战性的任务和数据集上评估了该方法，并与八种针对噪声标签设计的竞争性基线进行了比较。结果表明，FHLR在从噪声标签学习时性能显著提升，并以较大优势达到最优水平，在对称和非对称噪声下准确率提升高达19%。值得注意的是，我们发现FHLR对增加的标签噪声特别鲁棒，这与先前工作中严重性能退化的情况不同。本研究不仅在高风险健康感知基准测试中实现了更好的泛化，还揭示了噪声如何影响常用模型。