Unsupervised domain adaptation for robust sensory systems

Akhil Mathur, A Isopoussu, Nadia Berthouze, Nic Lane, F Kawsar
in UbiComp/ISWC '19 Adjunct Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing [and] Proceedings of the 2019 ACM International Symposium on Wearable Computers Pages 505-509, Conference paper (text), September 09 - 13, 2019


© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Despite signicant advances in the performance of sensory inference models, their poor robustness to changing environmental conditions and hardware remains a major hurdle for widespread adoption. In this paper, we introduce the concept of unsupervised domain adaptation which is a technique to adapt sensory inference models to new domains only using unlabeled data from the target domain. We present two case-studies to motivate the problem and highlight some of our recent work in this space. Finally, we discuss the core challenges in this space that can trigger further ubicomp research on this topic.