  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.
