  Understanding the structure of multiple related tasks allows for multi-task
learning to improve the generalisation ability of one or all of them. However,
it usually requires training each pairwise combination of tasks together in
order to capture task relationships, at an extremely high computational cost.
In this work, we learn task relationships via an automated weighting framework,
named Auto-Lambda. Unlike previous methods where task relationships are assumed
to be fixed, Auto-Lambda is a gradient-based meta learning framework which
explores continuous, dynamic task relationships via task-specific weightings,
and can optimise any choice of combination of tasks through the formulation of
a meta-loss; where the validation loss automatically influences task weightings
throughout training. We apply the proposed framework to both multi-task and
auxiliary learning problems in computer vision and robotics, and show that
Auto-Lambda achieves state-of-the-art performance, even when compared to
optimisation strategies designed specifically for each problem and data domain.
Finally, we observe that Auto-Lambda can discover interesting learning
behaviors, leading to new insights in multi-task learning. Code is available at
https://github.com/lorenmt/auto-lambda.
