Cross-domain neural-kernel networks

Siamak Mehrkanoon*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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 languageEnglish
Title of host publication31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning
Subtitle of host publicationBNAIC/BENELEARN 2019
PublisherCEUR-WS.org
Volume2491
Publication statusPublished - 1 Jan 2019
Event31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019
Conference number: 31

Publication series

SeriesCEUR Workshop Proceedings
ISSN1613-0073

Conference

Conference31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning
Abbreviated titleBNAIC/BENELEARN 2019
Country/TerritoryBelgium
CityBrussels
Period6/11/198/11/19

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