SWL-Adapt：一种用于跨用户可穿戴人体活动识别的带样本权重学习的无监督域适应模型
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
AI总结：
针对跨用户可穿戴活动识别中的用户差异与标注稀缺，提出 SWL-Adapt，通过元优化学习样本权重以选择性域对齐，并在三个数据集上取得最优性能。
摘要：
在实际应用中，可穿戴人体活动识别（WHAR）模型通常会因用户差异而在新用户上面临性能下降。在标注稀缺的情况下，无监督域适应（UDA）成为解决跨用户 WHAR 的自然方案。现有的 UDA 模型通常不加区分地对齐跨域样本，忽视了样本之间的差异。本文提出一种用于跨用户 WHAR 的带样本权重学习的无监督域适应模型（SWL-Adapt）。SWL-Adapt 通过参数化网络，根据每个样本的分类损失和域判别损失计算样本权重。我们引入基于元优化的更新规则来端到端地学习该网络，并以所选伪标签目标样本上的元分类损失为指导。因此，该网络能够根据当前跨用户 WHAR 任务拟合权重函数，优于现有针对特定场景固定的样本区分规则。在三个公开 WHAR 数据集上的大量实验表明，SWL-Adapt 在跨用户 WHAR 任务上取得了最先进性能，在准确率和宏 F1 分数上分别比最佳基线平均高出 3.1% 和 5.3%。
英文摘要：
In practice, Wearable Human Activity Recognition (WHAR) models usually face performance degradation on the new user due to user variance. Unsupervised domain adaptation (UDA) becomes the natural solution to cross-user WHAR under annotation scarcity. Existing UDA models usually align samples across domains without differentiation, which ignores the difference among samples. In this paper, we propose an unsupervised domain adaptation model with sample weight learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network. We introduce the meta-optimization based update rule to learn this network end-to-end, which is guided by meta-classification loss on the selected pseudo-labeled target samples. Therefore, this network can fit a weighting function according to the cross-user WHAR task at hand, which is superior to existing sample differentiation rules fixed for special scenarios. Extensive experiments on three public WHAR datasets demonstrate that SWL-Adapt achieves the state-of-the-art performance on the cross-user WHAR task, outperforming the best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score, respectively.
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。
