  Multi-task learning has gained popularity due to the advantages it provides
with respect to resource usage and performance. Nonetheless, the joint
optimization of parameters with respect to multiple tasks remains an active
research topic. Sub-partitioning the parameters between different tasks has
proven to be an efficient way to relax the optimization constraints over the
shared weights, may the partitions be disjoint or overlapping. However, one
drawback of this approach is that it can weaken the inductive bias generally
set up by the joint task optimization. In this work, we present a novel way to
partition the parameter space without weakening the inductive bias.
Specifically, we propose Maximum Roaming, a method inspired by dropout that
randomly varies the parameter partitioning, while forcing them to visit as many
tasks as possible at a regulated frequency, so that the network fully adapts to
each update. We study the properties of our method through experiments on a
variety of visual multi-task data sets. Experimental results suggest that the
regularization brought by roaming has more impact on performance than usual
partitioning optimization strategies. The overall method is flexible, easily
applicable, provides superior regularization and consistently achieves improved
performances compared to recent multi-task learning formulations.
