Date(s) : 06/11/2015 iCal
14 h 00 min - 15 h 00 min
Title: Optimal transport for domain adaptation\n\nAbstract: 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 representations become more robust when confronted to data depicting the same semantic concepts (the classes)\, but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another\, finding domain-invariant representations has shown excellent properties\, as a single classifier can use labelled samples from the source domain under this representation to predict the unlabelled samples of the target domain. 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 labelled samples in the source domain to remain close during transport. This way\, we exploit at the same time the few labeled information in the source and distributions of the input/observation variables observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method\, that consistently outperforms state of the art approaches.
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