Unsupervised domain adaptation with Joint Adversarial Variational AutoEncoder

Unsupervised domain adaptation techniques increase the classification performance of tasks from the target domain by utilizing the information in a related source domain. Since the target labeled samples are unavailable, matching similar samples across different domains effectively becomes increasingly hard, which retards the progress in this field. In this paper, we propose a Joint Adversarial Variational AutoEncoder (JVA²E) for unsupervised domain adaptation tasks. JVA²E chooses variational autoencoder as the basic framework to improve the generative ability. Both the marginal and conditional distributions are considered for joint distribution adaptation. The Wasserstein distance is chosen for improving the final performance. Multiple unique classifiers are carefully designed for generating pseudo labels which are utilized to increase intra-class similarity as well as narrow conditional distribution. Experiments are conducted on three publicly available datasets and the final results are compared with some state-of-the-art techniques. It illustrates that our proposed method yields better performances for most tasks against previous domain adaptation methods.