Multi-task learning (MTL) can improve the generalization performance of neural networks by sharing representations with related tasks. Nonetheless, MTL can also degrade performance through harmful interference between tasks. Recent work has pursued task-specific loss weighting as a solution for this interference. However, existing algorithms treat tasks as atomic, lacking the ability to explicitly separate harmful and helpful signals beyond the task level. To this end, we propose SLGrad, a sample-level weighting algorithm for multi-task learning with auxiliary tasks. Through sample-specific task weights, SLGrad reshapes the task distributions during training to eliminate harmful auxiliary signals and augment useful task signals. Substantial generalization performance gains are observed on (semi-) synthetic datasets and common supervised multi-task problems.

翻译：多任务学习（MTL）通过共享不同任务间的表示能够提升神经网络的泛化性能。然而，任务间的有害干扰也可能导致MTL性能下降。近期研究将任务特定损失加权作为解决该干扰问题的方案，但现有算法将任务视为原子单元，无法在任务层面之外显式区分有害与有益信号。为此，我们提出SLGrad——一种面向辅助任务的多任务学习样本级加权算法。通过样本特定的任务权重，SLGrad重塑训练过程中的任务分布，以消除有害辅助信号并增强有用任务信号。在（半）合成数据集及常见监督多任务问题中，该方法展现出显著的泛化性能提升。