  Multi-task learning (MTL) optimizes several learning tasks simultaneously and
leverages their shared information to improve generalization and the prediction
of the model for each task. Auxiliary tasks can be added to the main task to
ultimately boost the performance. In this paper, we provide a brief review on
the recent deep multi-task learning (dMTL) approaches followed by methods on
selecting useful auxiliary tasks that can be used in dMTL to improve the
performance of the model for the main task.
