Domain adaptation (DA) considers the problem of adapting a machine learning model such as a classifier that is trained using a source domain to a target domain.
in Unsupervised Domain Adaptation (UDA) (Blitzer et al., 2006(Blitzer et al., , 2007Pan et al., 2010) we do not assume the availability of any labelled instances from the target domain but a set of labelled instances from the source domain and unlabelled instances from both source and the target domains.