Domain Adaptation (DA) exploits labeled data and models from similar domains in order to alleviate the annotation burden when learning a model in a new domain. Our contribution to the field is ...

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the source domain.