Abstract
Semi-supervised learning is crucial for alleviating la-belling 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.
Original language | English |
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Title of host publication | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
Number of pages | 8 |
Publisher | AAAI Press |
Publication date | 2019 |
Pages | 3321-3328 |
ISBN (Electronic) | 9781577358091 |
Publication status | Published - 2019 |
Event | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 - Honolulu, United States Duration: 27 Jan 2019 → 1 Feb 2019 |
Conference
Conference | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Country/Territory | United States |
City | Honolulu |
Period | 27/01/2019 → 01/02/2019 |
Sponsor | Association for the Advancement of Artificial Intelligence |
Series | 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 |
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Bibliographical note
Publisher Copyright:© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org).