Manifold Embedded Distribution Alignment (MEDA) approach aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain . MEDA learns a domain-invariant classifier in Grassmann manifold with structural risk minimization . Extensive experiments demonstrate that MEDA shows significant improvements in classification accuracy compared to state-of-the-art traditional and deep methods . To the best of our knowledge, MEDA is the first attempt to perform dynamic distribution alignment for manifold domain adaptation. Extensive experiments demonstrated improvements in classification accuracy competitiveness against state-of theart traditional and deep methods of traditional and deep methods . For more information on MEDA, visit http://www.meda.com/medea.org/medela. For more details, please visit www.medelaonline.com.com . Back to the page you came from

Links: PDF - Abstract

Code :

https://github.com/jindongwang/transferlearning

Keywords : meda - domain - manifold - deep - distribution -

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