Efficiently Identifying Task Groupings for Multi-Task Learning

Abstract

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

In NeurIPS 2021 (spotlight). Code is available at https://github.com/google-research/google-research/tree/master/tag

References in corpus (6)

• An Overview of Multi-Task Learning in Deep Neural Networks
• Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
• Pareto Multi-Task Learning
• Understanding and Improving Information Transfer in Multi-Task Learning
• Learning to Branch for Multi-Task Learning
• Regularizing Deep Multi-Task Networks using Orthogonal Gradients