  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
because some groupings might produce performance degradation due to negative
interference between tasks. That is why 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 a re-proposed data-driven features, Data Maps, which capture the training
dynamics for each classification task during the MTL training. Through a
theoretical comparison with other techniques, we manage to show that our
approach has the superior scalability. Our experiments show a better
performance and verify the method's effectiveness, even on an unprecedented
number of tasks (up to 100 tasks on CIFAR100). Being the first to work on such
number of tasks, our comparisons on the resulting grouping shows similar
grouping to the mentioned in the dataset, CIFAR100. Finally, we provide a
modular implementation for easier integration and testing, with examples from
multiple datasets and tasks.
