Abstract
A novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem is introduced. The proposed model consists of two stream neural-kernel networks corresponding to the source and target domains which are enriched with a coupling term. Each stream neural-kernel networks follows a combination of neural network layer and an explicit feature map constructed by means of random Fourier features. The introduced coupling term aims at enforcing correlations among the output of the intermediate layers of the two stream networks as well as encouraging the two networks to learn shared representation of the data from both source and target domains. Experimental results are given to illustrate the effectiveness of the proposed approaches on real-life datasets.
Original language | English |
---|---|
Title of host publication | 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning |
Subtitle of host publication | BNAIC/BENELEARN 2019 |
Publisher | CEUR-WS.org |
Volume | 2491 |
Publication status | Published - 1 Jan 2019 |
Event | 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning - Brussels, Belgium Duration: 6 Nov 2019 → 8 Nov 2019 Conference number: 31 |
Publication series
Series | CEUR Workshop Proceedings |
---|---|
ISSN | 1613-0073 |
Conference
Conference | 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning |
---|---|
Abbreviated title | BNAIC/BENELEARN 2019 |
Country/Territory | Belgium |
City | Brussels |
Period | 6/11/19 → 8/11/19 |