  Training neural networks with auxiliary tasks is a common practice for
improving the performance on a main task of interest. Two main challenges arise
in this multi-task learning setting: (i) designing useful auxiliary tasks; and
(ii) combining auxiliary tasks into a single coherent loss. Here, we propose a
novel framework, AuxiLearn, that targets both challenges based on implicit
differentiation. First, when useful auxiliaries are known, we propose learning
a network that combines all losses into a single coherent objective function.
This network can learn non-linear interactions between tasks. Second, when no
useful auxiliary task is known, we describe how to learn a network that
generates a meaningful, novel auxiliary task. We evaluate AuxiLearn in a series
of tasks and domains, including image segmentation and learning with attributes
in the low data regime, and find that it consistently outperforms competing
methods.
