  In this paper, we propose a dual-module network architecture that employs a
domain discriminative feature module to encourage the domain invariant feature
module to learn more domain invariant features. The proposed architecture can
be applied to any model that utilizes domain invariant features for
unsupervised domain adaptation to improve its ability to extract domain
invariant features. We conduct experiments with the Domain-Adversarial Training
of Neural Networks (DANN) model as a representative algorithm. In the training
process, we supply the same input to the two modules and then extract their
feature distribution and prediction results respectively. We propose a
discrepancy loss to find the discrepancy of the prediction results and the
feature distribution between the two modules. Through the adversarial training
by maximizing the loss of their feature distribution and minimizing the
discrepancy of their prediction results, the two modules are encouraged to
learn more domain discriminative and domain invariant features respectively.
Extensive comparative evaluations are conducted and the proposed approach
outperforms the state-of-the-art in most unsupervised domain adaptation tasks.
