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Multi-task learning can leverage information learned by one task to benefitthe training of other tasks. Despite this capacity, naively training all taskstogether in one model often degrades performance, and exhaustively searchingthrough combinations of task groupings can be prohibitively expensive. As aresult, efficiently identifying the tasks that would benefit from trainingtogether remains a challenging design question without a clear solution. Inthis paper, we suggest an approach to select which tasks should train togetherin multi-task learning models. Our method determines task groupings in a singlerun by training all tasks together and quantifying the effect to which onetask's gradient would affect another task's loss. On the large-scale Taskonomycomputer vision dataset, we find this method can decrease test loss by 10.0%compared to simply training all tasks together while operating 11.6 timesfaster than a state-of-the-art task grouping method.

Efficiently Identifying Task Groupings for Multi-Task Learning