Importance-weighted conditional adversarial network for unsupervised domain adaptation

In the construction of expert and intelligent systems, annotating and curating large datasets is very expensive; hence, there is a need to transfer the knowledge from existing annotated datasets to unlabeled data. However, data that are relevant for a specific application usually differ from publicly available datasets because they are sampled from a different domain. Domain adaptation (DA) has emerged as an efficient technique to compensate for...