Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation

Understand

We study the problem of unsupervised domain adaptation, which aims to adapt classifiers trained on a labeled source domain to an unlabeled target domain.

• Many existing approaches first learn domain-invariant features and then construct classifiers with them.
• We propose a novel approach that jointly learn the both.
• Specifically, while the method identifies a feature space where data in the source and the target domains are similarly distributed, it also learns the feature space discriminatively, optimizing an information-theoretic metric as an proxy to the expected misclassification error on the target domain.