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.

该文章研究了无监督领域自适应的问题，提出一种新的方法，该方法同时学习 domain-invariant features 和 discriminative features，并且以信息论度量作为优化目标，在目标域和源域中训练分类器。经过实验验证，该方法在物体识别和情感分析等任务中表现出了明显的优势。