Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
This addresses the challenge of labeling burdens in human-generated sensing data, which suffers from distribution shifts due to diverse biological and behavioral patterns, representing a domain-specific incremental improvement.
The paper tackles the problem of distribution shift in semi-supervised learning for people-centric sensing data, proposing a distributionally robust model that reduces person-specific discrepancy while preserving task-specific consistency. The model outperforms state-of-the-art methods across multiple real-world recognition tasks including intention, activity, muscular movement, and gesture recognition.
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.