English
资源说明：Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly,
models built on a specific data representation become more robust when confronted to data depicting the same classes, but
described by another observation system. Among the many strategies proposed, finding domain-invariant representations has
shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we
propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source
and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class
in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source
and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show
the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show
that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the
semi-supervised case where few labeled samples are available in the target domain.
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