  In this work, we present Con$^{2}$DA, a simple framework that extends recent
advances in semi-supervised learning to the semi-supervised domain adaptation
(SSDA) problem. Our framework generates pairs of associated samples by
performing stochastic data transformations to a given input. Associated data
pairs are mapped to a feature representation space using a feature extractor.
We use different loss functions to enforce consistency between the feature
representations of associated data pairs of samples. We show that these learned
representations are useful to deal with differences in data distributions in
the domain adaptation problem. We performed experiments to study the main
components of our model and we show that (i) learning of the consistent and
contrastive feature representations is crucial to extract good discriminative
features across different domains, and ii) our model benefits from the use of
strong augmentation policies. With these findings, our method achieves
state-of-the-art performances in three benchmark datasets for SSDA.
