Name
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3345609 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
activity recognition, audio sensing, domain adaptation, neural networks, unsupervised learning | Computer vision,Activity recognition,Domain adaptation,Audio sensing,Computer science,Speech recognition,Unsupervised learning,Artificial intelligence,Artificial neural network,Sensory system | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akhil Mathur | 1 | 101 | 15.10 |
| Anton Isopoussu | 2 | 2 | 2.08 |
| Nadia Berthouze | 3 | 123 | 14.38 |
| Nicholas D. Lane | 4 | 4247 | 248.15 |
| Fahim Kawsar | 5 | 909 | 80.24 |