STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
Ammar Sherif Abubakar Abid Mustafa Elattar Mohamed ElHelw
Multi-Task Learning (MTL) is a powerful technique that has gained popularity
due to its performance improvement over traditional Single-Task Learning (STL).
However, MTL is often challenging because there is an exponential number of
possible task groupings, which can make it difficult to choose the best one,
and some groupings might produce performance degradation due to negative
interference between tasks. Furthermore, existing solutions are severely
suffering from scalability issues, limiting any practical application. In our
paper, we propose a new data-driven method that addresses these challenges and
provides a scalable and modular solution for classification task grouping based
on hand-crafted features, specifically Data Maps, which capture the training
behavior for each classification task during the MTL training. We experiment
with the method demonstrating its effectiveness, even on an unprecedented
number of tasks (up to 100).