Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks enables reducing the amount of data required to learn a new task. We here propose a method for learning a mapping model that maps data from a source task with labeled data to a related target task with only unlabeled data. We perform an empirical evaluation showing that our method achieves performance comparable to a model learned directly in the target task.