  Unsupervised domain adaptation aims at transferring knowledge from the
labeled source domain to the unlabeled target domain. Previous adversarial
domain adaptation methods mostly adopt the discriminator with binary or
$K$-dimensional output to perform marginal or conditional alignment
independently. Recent experiments have shown that when the discriminator is
provided with domain information in both domains and label information in the
source domain, it is able to preserve the complex multimodal information and
high semantic information in both domains. Following this idea, we adopt a
discriminator with $2K$-dimensional output to perform both domain-level and
class-level alignments simultaneously in a single discriminator. However, a
single discriminator can not capture all the useful information across domains
and the relationships between the examples and the decision boundary are rarely
explored before. Inspired by multi-view learning and latest advances in domain
adaptation, besides the adversarial process between the discriminator and the
feature extractor, we also design a novel mechanism to make two discriminators
pit against each other, so that they can provide diverse information for each
other and avoid generating target features outside the support of the source
domain. To the best of our knowledge, it is the first time to explore a dual
adversarial strategy in domain adaptation. Moreover, we also use the
semi-supervised learning regularization to make the representations more
discriminative. Comprehensive experiments on two real-world datasets verify
that our method outperforms several state-of-the-art domain adaptation methods.
