Understanding the structure of multiple related tasks allows for multi-task learning to improve the generalisation ability of one or all of them. However, it usually requires training each pairwise combination of tasks together in order to capture task relationships, at an extremely high computational cost. In this work, we learn task relationships via an automated weighting framework, named Auto-Lambda. Unlike previous methods where task relationships are assumed to be fixed, Auto-Lambda is a gradient-based meta learning framework which explores continuous, dynamic task relationships via task-specific weightings, and can optimise any choice of combination of tasks through the formulation of a meta-loss; where the validation loss automatically influences task weightings throughout training. We apply the proposed framework to both multi-task and auxiliary learning problems in computer vision and robotics, and show that Auto-Lambda achieves state-of-the-art performance, even when compared to optimisation strategies designed specifically for each problem and data domain. Finally, we observe that Auto-Lambda can discover interesting learning behaviors, leading to new insights in multi-task learning. Code is available at https://github.com/lorenmt/auto-lambda.
翻译:了解多重相关任务的结构,可以进行多任务学习,以提高其中一人或所有人的总体能力。然而,通常需要以极高的计算成本对每个任务进行对齐组合,以捕捉任务关系。在这项工作中,我们通过自动加权框架(Auto-Lambda)学习任务关系。与以前假定任务关系可以固定的方法不同,Auto-Lambda是一个基于梯度的元学习框架,它通过特定任务加权来探索持续、动态的任务关系,并且可以通过制定元损失来优化任务组合的任何选择;在这种情况下,验证损失自动影响整个培训中的任务加权。我们将拟议框架应用于计算机视觉和机器人的多任务和辅助学习问题,并显示Auto-Lambda取得了最新业绩,即使与专门为每个问题和数据领域设计的优化战略相比,Auto-Lambda也可以发现有趣的学习行为,导致多任务/数字数字学习的新洞察力。我们可在 https://gima/toima上找到代码。