no code implementations • 22 Sep 2020 • Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source data is provided.
1 code implementation • 21 Sep 2020 • Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn
We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected.