Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated 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.

Authors

• Kaixuan Chen State Key Laboratory of Blockchain and Data Security, Zhejiang University
• Lina Yao CSIRO’s Data61
• Dalin Zhang Aalborg University, Denmark
• Xiaojun Chang University of Science and Technology of China Mohamed Bin Zayed University of Artificial Intelligenc
• Guodong Long Australian Artificial Intelligence Institute, University of Technology Sydney
• Sen Wang

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