Connecting the dots without clues: Unsupervised domain adaptation for cross-domain visual classification

Many real-world visual classification tasks require one to recognize test data in a particular domain of interest, while the training data can only be collected from a different domain. This can be viewed as the problem of unsupervised domain adaptation, in which the domain difference and the lack of cross-domain label/correspondence information make the recognition task very difficult. In this paper, we propose to exploit the cross-domain data correspondence using both observed data similarity and labels transferred from the source domain. This allows us to perform distribution matching for cross-domain data with recognition guarantees. Our experiments on three different cross-domain visual classification tasks would confirm the effectiveness of our method, which is shown to perform favorably against state-of-the-art unsupervised domain adaptation approaches.