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Published in:
2017 | OriginalPaper | Chapter
6. Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation
Authors : Nazli Farajidavar, Teofilo de Campos, Josef Kittler
Published in: Domain Adaptation in Computer Vision Applications
Publisher: Springer International Publishing
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Abstract
This chapter addresses the problem of transfer learning by unsupervised domain adaptation. We introduce a pipeline which is designed for the case where the joint distribution of samples and labels \(P(\mathbf {X}^{src},\mathbf {Y}^{src})\) in the source domain is assumed to be different, but related to that of a target domain \(P(\mathbf {X}^{ trg },\mathbf {Y}^{ trg })\), but labels \(\mathbf {Y}^{ trg }\) are not available for the target set. This is a problem of Transductive Transfer Learning. In contrast to other methodologies in this book, our method combines steps that adapt both the marginal and the conditional distributions of the data.