  Multi-task learning, as it is understood nowadays, consists of using one
single model to carry out several similar tasks. From classifying hand-written
characters of different alphabets to figuring out how to play several Atari
games using reinforcement learning, multi-task models have been able to widen
their performance range across different tasks, although these tasks are
usually of a similar nature. In this work, we attempt to widen this range even
further, by including heterogeneous tasks in a single learning procedure. To do
so, we firstly formally define a multi-network model, identifying the necessary
components and characteristics to allow different adaptations of said model
depending on the tasks it is required to fulfill. Secondly, employing the
formal definition as a starting point, we develop an illustrative model example
consisting of three different tasks (classification, regression and data
sampling). The performance of this model implementation is then analyzed,
showing its capabilities. Motivated by the results of the analysis, we
enumerate a set of open challenges and future research lines over which the
full potential of the proposed model definition can be exploited.
