While multi-task learning (MTL) has gained significant attention in recent years, its underlying mechanisms remain poorly understood. Recent methods did not yield consistent performance improvements over single task learning (STL) baselines, underscoring the importance of gaining more profound insights about challenges specific to MTL. In our study, we challenge paradigms in MTL in the context of STL: First, the impact of the choice of optimizer has only been mildly investigated in MTL. We show the pivotal role of common STL tools such as the Adam optimizer in MTL empirically in various experiments. To further investigate Adam's effectiveness, we theoretical derive a partial loss-scale invariance under mild assumptions. Second, the notion of gradient conflicts has often been phrased as a specific problem in MTL. We delve into the role of gradient conflicts in MTL and compare it to STL. For angular gradient alignment we find no evidence that this is a unique problem in MTL. We emphasize differences in gradient magnitude as the main distinguishing factor. Lastly, we compare the transferability of features learned through MTL and STL on common image corruptions, and find light evidence that MTL can lead to superior transferability. Overall, we find surprising similarities between STL and MTL suggesting to consider methods from both fields in a broader context.

翻译：尽管多任务学习（MTL）近年来备受关注，但其内在机制仍未被充分理解。近期方法未能相对于单任务学习（STL）基线持续提升性能，这凸显了深入理解MTL特有挑战的重要性。本研究在STL背景下对MTL范式提出质疑：首先，优化器选择对MTL的影响此前仅得到有限探究。我们通过多项实验实证表明，Adam优化器等常见STL工具在MTL中具有关键作用。为深入探究Adam的有效性，我们在弱假设条件下理论推导出部分损失尺度不变性。其次，“梯度冲突”概念常被表述为MTL的特有问题。我们深入剖析梯度冲突在MTL中的作用，并将其与STL进行对比。针对梯度方向对齐现象，未发现这是MTL特有问题的证据，而梯度幅值差异才是核心区分因素。最后，我们比较了MTL与STL学习特征在常见图像退化场景中的迁移能力，初步证据表明MTL可能具有更强的特征迁移性。总体而言，STL与MTL之间存在惊人相似性，这启示我们应在更广泛视角下审视这两个领域的方法。