  Accurately identifying different representations of the same real-world
entity is an integral part of data cleaning and many methods have been proposed
to accomplish it. The challenges of this entity resolution task that demand so
much research attention are often rooted in the task-specificity and
user-dependence of the process. Adopting deep learning techniques has the
potential to lessen these challenges. In this paper, we set out to devise an
entity resolution method that builds on the robustness conferred by deep
autoencoders to reduce human-involvement costs. Specifically, we reduce the
cost of training deep entity resolution models by performing unsupervised
representation learning. This unveils a transferability property of the
resulting model that can further reduce the cost of applying the approach to
new datasets by means of transfer learning. Finally, we reduce the cost of
labelling training data through an active learning approach that builds on the
properties conferred by the use of deep autoencoders. Empirical evaluation
confirms the accomplishment of our cost-reduction desideratum while achieving
comparable effectiveness with state-of-the-art alternatives.
