Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.

提出一种针对分布偏移数据应用在半监督学习的通用分布鲁棒模型，考虑标记数据和未标记数据的不一致和一致性，学习减少个体差异和保留任务特定一致性的潜在特征，并在实际数据集上对意图识别、活动识别、肌肉运动识别和手势识别等多种人类中心识别任务进行评估，结果表明该模型优于现有方法。