  Multi-Task Learning (MTL) is a framework, where multiple related tasks are
learned jointly and benefit from a shared representation space, or parameter
transfer. To provide sufficient learning support, modern MTL uses annotated
data with full, or sufficiently large overlap across tasks, i.e., each input
sample is annotated for all, or most of the tasks. However, collecting such
annotations is prohibitive in many real applications, and cannot benefit from
datasets available for individual tasks. In this work, we challenge this setup
and show that MTL can be successful with classification tasks with little, or
non-overlapping annotations, or when there is big discrepancy in the size of
labeled data per task. We explore task-relatedness for co-annotation and
co-training, and propose a novel approach, where knowledge exchange is enabled
between the tasks via distribution matching. To demonstrate the general
applicability of our method, we conducted diverse case studies in the domains
of affective computing, face recognition, species recognition, and shopping
item classification using nine datasets. Our large-scale study of affective
tasks for basic expression recognition and facial action unit detection
illustrates that our approach is network agnostic and brings large performance
improvements compared to the state-of-the-art in both tasks and across all
studied databases. In all case studies, we show that co-training via
task-relatedness is advantageous and prevents negative transfer (which occurs
when MT model's performance is worse than that of at least one single-task
model).
