In Multitask Learning (MTL,) one factor that can have an effect on the performance of an MTL architecture is how correlated or uncorrelated tasks are. As far as I understand, the more correlated tasks are, the less performance improvement we will be expecting to see (correct me if I am wrong).
Then I wonder, when choosing different tasks for MTL, how should we know how correlated our tasks are? I'm asking since ideally we want to train a network to give us maximum performance improvement but without knowing whether tasks are highly correlated, we may not achieve this.
Is there any way to measure the "correlation" between tasks? Or should we just simply compare tasks' input and output and judge if they are similar or different?
Citation about task correlation (Caruana's MTL paper, 1997):
Caruana, R. Multitask Learning. Machine Learning 28, 41–75 (1997)