  Data collection and annotation are time-consuming in machine learning,
expecially for large scale problem. A common approach for this problem is to
transfer knowledge from a related labeled domain to a target one. There are two
popular ways to achieve this goal: adversarial learning and self training. In
this article, we first analyze the training unstablity problem and the mistaken
confusion issue in adversarial learning process. Then, inspired by domain
confusion and self-ensembling methods, we propose a combined model to learn
feature and class jointly invariant representation, namely Domain Confusion
with Self Ensembling (DCSE). The experiments verified that our proposed
approach can offer better performance than empirical art in a variety of
unsupervised domain adaptation benchmarks.
