UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Unsupervised domain adaptation for robust sensory systems

Mathur, A; Isopoussu, A; Berthouze, N; Lane, ND; Kawsar, F; (2019) Unsupervised domain adaptation for robust sensory systems. In: Harle, R and Farrahi, K and Lane, N, (eds.) 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. (pp. pp. 505-509). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

[thumbnail of cmliot (3).pdf]
Preview
Text
cmliot (3).pdf - Accepted Version

Download (517kB) | Preview

Abstract

Despite significant 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.

Type: Proceedings paper
Title: Unsupervised domain adaptation for robust sensory systems
Event: 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC '19)
ISBN-13: 9781450368698
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3341162.3345609
Publisher version: https://doi.org/10.1145/3341162.3345609
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: domain adaptation; neural networks; audio sensing; activity recognition; unsupervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10084334
Downloads since deposit
172Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item