  Model selection is a problem that has occupied machine learning researchers
for a long time. Recently, its importance has become evident through
applications in deep learning. We propose an agreement-based learning framework
that prevents many of the pitfalls associated with model selection. It relies
on coupling the training of multiple models by encouraging them to agree on
their predictions while training. In contrast with other model selection and
combination approaches used in machine learning, the proposed framework is
inspired by human learning. We also propose a learning algorithm defined within
this framework which manages to significantly outperform alternatives in
practice, and whose performance improves further with the availability of
unlabeled data. Finally, we describe a number of potential directions for
developing more flexible agreement-based learning algorithms.
