Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task balancing problem remains a significant challenge in MTL, with the disparity in loss/gradient scales often leading to performance compromises. In this paper, we propose a Dual-Balancing Multi-Task Learning (DB-MTL) method to alleviate the task balancing problem from both loss and gradient perspectives. Specifically, DB-MTL ensures loss-scale balancing by performing a logarithm transformation on each task loss, and guarantees gradient-magnitude balancing via normalizing all task gradients to the same magnitude as the maximum gradient norm. Extensive experiments conducted on several benchmark datasets consistently demonstrate the state-of-the-art performance of DB-MTL.

翻译：多任务学习（MTL）是一种同时学习多个相关任务的学习范式，已在多个领域取得了显著成功。然而，任务平衡问题仍是MTL中的重大挑战，损失/梯度尺度的差异常导致性能折中。本文提出双平衡多任务学习（DB-MTL）方法，从损失与梯度两个维度缓解任务平衡问题。具体而言，DB-MTL通过对各任务损失进行对数变换实现损失尺度平衡，并通过将所有任务梯度归一化至与最大梯度范数相同的量级保证梯度幅值平衡。在多个基准数据集上进行的大量实验一致表明，DB-MTL达到了最先进的性能。