When Multi-Task Learning Meets Partial Supervision: A Computer Vision
  Review
Maxime Fontana Michael Spratling Miaojing Shi
Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while
exploiting their mutual relationships. By using shared resources to
simultaneously calculate multiple outputs, this learning paradigm has the
potential to have lower memory requirements and inference times compared to the
traditional approach of using separate methods for each task. Previous work in
MTL has mainly focused on fully-supervised methods, as task relationships can
not only be leveraged to lower the level of data-dependency of those methods
but they can also improve performance. However, MTL introduces a set of
challenges due to a complex optimisation scheme and a higher labeling
requirement. This review focuses on how MTL could be utilised under different
partial supervision settings to address these challenges. First, this review
analyses how MTL traditionally uses different parameter sharing techniques to
transfer knowledge in between tasks. Second, it presents the different
challenges arising from such a multi-objective optimisation scheme. Third, it
introduces how task groupings can be achieved by analysing task relationships.
Fourth, it focuses on how partially supervised methods applied to MTL can
tackle the aforementioned challenges. Lastly, this review presents the
available datasets, tools and benchmarking results of such methods.