Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent method is adversarial domain adaptation, which can shorten the distance between the source domain and the target domain. Although adversarial learning is very effective, it still leads to the instability of the network and the drawbacks of confusing category information. In this paper, we propose a Robust Ensembling Network (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer. Specifically, REN mainly includes a teacher network and a student network, which performs standard domain adaptation training and updates weights of the teacher network. In addition, we also propose a dual-network conditional adversarial loss to improve the ability of the discriminator. Finally, for the purpose of improving the basic ability of the student network, we utilize the consistency constraint to balance the error between the student network and the teacher network. Extensive experimental results on several UDA datasets have demonstrated the effectiveness of our model by comparing with other state-of-the-art UDA algorithms.

翻译：最近,为了解决无人监督的域适应(UDA)问题,提出了实现可转让模式的广泛研究建议,其中最普遍的方法是对抗性域适应,这可以缩短源域和目标域之间的距离;虽然对抗性学习非常有效,但仍然导致网络不稳定,造成类别信息混乱的缺陷;在本文件中,我们提议为UDA建立一个强力集合网络(REN),这个网络运用一个强大的时间,汇集教师网络,学习域转让的全球信息;具体地说,RED主要包括一个教师网络和一个学生网络,进行标准的域适应培训和更新教师网络的权重;此外,我们还提出一个双网络有条件的对抗性损失,以提高歧视者的能力;最后,为了提高学生网络的基本能力,我们利用一致性限制来平衡学生网络和教师网络之间的错误。