In multi-task learning, multiple tasks are solved jointly, sharing inductive
bias between them. Multi-task learning is inherently a multi-objective problem
because different tasks may conflict, necessitating a trade-off. A common
compromise is to optimize a proxy objective that minimizes a weighted linear
combination of per-task losses. However, this workaround is only valid when the
tasks do not compete, which is rarely the case. In this paper, we explicitly
cast multi-task learning as multi-objective optimization, with the overall
objective of finding a Pareto optimal solution. To this end, we use algorithms
developed in the gradient-based multi-objective optimization literature. These
algorithms are not directly applicable to large-scale learning problems since
they scale poorly with the dimensionality of the gradients and the number of
tasks. We therefore propose an upper bound for the multi-objective loss and
show that it can be optimized efficiently. We further prove that optimizing
this upper bound yields a Pareto optimal solution under realistic assumptions.
We apply our method to a variety of multi-task deep learning problems including
digit classification, scene understanding (joint semantic segmentation,
instance segmentation, and depth estimation), and multi-label classification.
Our method produces higher-performing models than recent multi-task learning
formulations or per-task training.

本论文将多任务学习作为多目标优化来解决，并提出了一种基于梯度下降优化算法的上限边界，证明了在现实情况下优化这个上限边界将得到帕累托最优解，在多任务深度学习问题上应用我们的方法表现出比最近的多任务学习公式或每项任务训练的模型更高的性能。