  Semi-supervised domain adaptation (SSDA) aims to adapt models trained from a
labeled source domain to a different but related target domain, from which
unlabeled data and a small set of labeled data are provided. Current methods
that treat source and target supervision without distinction overlook their
inherent discrepancy, resulting in a source-dominated model that has not
effectively used the target supervision. In this paper, we argue that the
labeled target data needs to be distinguished for effective SSDA, and propose
to explicitly decompose the SSDA task into two sub-tasks: a semi-supervised
learning (SSL) task in the target domain and an unsupervised domain adaptation
(UDA) task across domains. By doing so, the two sub-tasks can better leverage
the corresponding supervision and thus yield very different classifiers. To
integrate the strengths of the two classifiers, we apply the well-established
co-training framework, in which the two classifiers exchange their high
confident predictions to iteratively "teach each other" so that both
classifiers can excel in the target domain. We call our approach Deep
Co-training with Task decomposition (DeCoTa). DeCoTa requires no adversarial
training and is easy to implement. Moreover, DeCoTa is well-founded on the
theoretical condition of when co-training would succeed. As a result, DeCoTa
achieves state-of-the-art results on several SSDA datasets, outperforming the
prior art by a notable 4% margin on DomainNet. Code is available at
https://github.com/LoyoYang/DeCoTa
