TarGAN: Generating target data with class labels for unsupervised domain adaptation

Abstract

The recent successes of deep neural networks heavily rely on massive amounts of data annotations. For a certain task that is short of labeled data, the cost of large-scale data annotation is usually prohibitively expensive. Through transferring the already learnt knowledge from a different but related source domain that is sufficiently labeled, domain adaptation aims to relieve such a burden for the target domain. The recent studies reveal that learning domain-invariant features has gained great successes in unsupervised domain adaptation. However, domain invariance does not necessarily imply discriminative representations in the target domain due to the lack of labeled target data. To address this issue, we propose a new unsupervised domain adaptation method named TarGAN, which can successfully disentangle the class and the style codes of the target generator and generate Target samples with given class labels based on the recent advances of Generative Adversarial Networks. This is achieved by tying the high-level layers of the source generator and the target generator, and concurrently enforcing high mutual information between the class code and the generated images. Accompanied with given class labels, these generated target images can significantly improve the classifier’s discriminative ability on the target domain. Experiments on several standard benchmarks demonstrate that our proposed TarGAN can outperform the existing state-of-the-art domain adaptation methods.