Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task-balancing remains a significant challenge in MTL, with the disparity in loss/gradient scales often leading to performance compromises. In this paper, we propose a Scale-Invariant Multi-Task Learning (SI-MTL) method to alleviate the task-balancing problem from both loss and gradient perspectives. Specifically, SI-MTL contains a logarithm transformation which is performed on all task losses to ensure scale-invariant at the loss level, and a gradient balancing method, SI-G, which normalizes all task gradients to the same magnitude as the maximum gradient norm. Extensive experiments conducted on several benchmark datasets consistently demonstrate the effectiveness of SI-G and the state-of-the-art performance of SI-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，已在各个领域取得了巨大成功。然而，任务平衡仍然是MTL中的重大挑战，损失/梯度尺度的差异往往导致性能折中。本文提出了一种尺度不变的多任务学习（SI-MTL）方法，从损失和梯度两个角度缓解任务平衡问题。具体而言，SI-MTL包含两个部分：对所有任务损失进行对数变换，以实现损失层面的尺度不变性；以及一种梯度平衡方法SI-G，该方法将所有任务梯度归一化至与最大梯度范数相同的量级。在多个基准数据集上进行的大量实验一致证明了SI-G的有效性以及SI-MTL的先进性能。