In deep learning, auxiliary objectives are often used to facilitate learning in situations where data is scarce, or the principal task is extremely complex. This idea is primarily inspired by the improved generalization capability induced by solving multiple tasks simultaneously, which leads to a more robust shared representation. Nevertheless, finding optimal auxiliary tasks that give rise to the desired improvement is a crucial problem that often requires hand-crafted solutions or expensive meta-learning approaches. In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model. The disentanglement procedure works at a representation level, isolating a subspace related to the principal task, plus an arbitrary number of orthogonal subspaces. In the most disentangled subspaces, through a clustering procedure, we generate the additional classification tasks, and the associated labels become their representatives. Subsequently, the original data, the labels associated with the principal task, and the newly discovered ones can be fed into any MTL framework. Extensive validation on both synthetic and real data, along with various ablation studies, demonstrate promising results, revealing the potential in what has been, so far, an unexplored connection between learning disentangled representations and MTL. The code will be made publicly available upon acceptance.

翻译：在深度学习中，辅助目标常被用于促进数据稀缺或主任务极其复杂情况下的学习。这一思想主要源于同时解决多个任务能够改善泛化能力，从而产生更鲁棒的共享表征。然而，寻找能带来预期改进的最优辅助任务是一个关键问题，通常需要手工设计的解决方案或昂贵的元学习方法。本文提出一种名为Detaux的新框架，通过弱监督解耦过程发现新的无关分类任务及其相关标签，这些标签可与主任务一起在任何多任务学习模型中使用。该解耦过程在表征层面进行，隔离与主任务相关的子空间，以及任意数量的正交子空间。在解耦最彻底的子空间中，通过聚类过程生成额外的分类任务，其相关标签成为这些任务的代表。随后，原始数据、与主任务相关的标签以及新发现的标签可被输入到任何多任务学习框架中。在合成数据和真实数据上的广泛验证以及多项消融研究均展现出令人鼓舞的结果，揭示了迄今尚未被探索的解耦表征学习与多任务学习之间联系所蕴含的潜力。代码将在论文被接收后公开发布。