Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation
This addresses the problem of adapting classifiers from labeled source to unlabeled target domains, with incremental improvements over existing methods.
The paper tackles unsupervised domain adaptation by jointly learning domain-invariant and discriminative features using an information-theoretic metric, resulting in significant improvements in classification accuracies on benchmark tasks like object recognition and sentiment analysis.
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. We show how this optimization can be effectively carried out with simple gradient-based methods and how hyperparameters can be cross-validated without demanding any labeled data from the target domain. Empirical studies on benchmark tasks of object recognition and sentiment analysis validated our modeling assumptions and demonstrated significant improvement of our method over competing ones in classification accuracies.