Methods addressing Learning with Noisy Labels (LNL) and multi-source Domain Generalization (DG) use training techniques to improve downstream task performance in the presence of label noise or domain shifts, respectively. Prior work often explores these tasks in isolation, and the limited work that does investigate their intersection, which we refer to as Noise-Aware Generalization (NAG), only benchmarks existing methods without also proposing an approach to reduce its effect. We find that this is likely due, in part, to the new challenges that arise when exploring NAG, which does not appear in LNL or DG alone. For example, we show that the effectiveness of DG methods is compromised in the presence of label noise, making them largely ineffective. Similarly, LNL methods often overfit to easy-to-learn domains as they confuse domain shifts for label noise. Instead, we propose Domain Labels for Noise Detection (DL4ND), the first direct method developed for NAG which uses our observation that noisy samples that may appear indistinguishable within a single domain often show greater variation when compared across domains. We find DL4ND outperforms DG and LNL methods, including their combinations, even when simplifying the NAG challenge by using domain labels to isolate domain shifts from noise. Performance gains up to 12.5% over seven diverse datasets with three noise types demonstrates DL4ND's ability to generalize to a wide variety of settings.

翻译：针对含噪标签学习与多源域泛化的方法分别通过训练技术提升模型在标签噪声或域偏移存在时的下游任务性能。先前研究常孤立探讨这两类任务，而有限的相关交叉研究（我们称之为噪声感知泛化）仅对现有方法进行基准测试，未提出缓解其影响的解决方案。我们发现这在一定程度上源于探索噪声感知泛化时出现的新挑战，这些挑战在单独的含噪标签学习或域泛化中并不显著。例如，我们证明域泛化方法在标签噪声存在时效果受损，基本失效；而含噪标签学习方法常将域偏移误判为标签噪声，导致对易学习域的过拟合。为此，我们提出首个直接面向噪声感知泛化的方法——基于域标签的噪声检测，该方法利用我们的关键发现：在单域内难以区分的噪声样本，在跨域对比中往往表现出更大变异度。实验表明，即使通过域标签分离域偏移与噪声以简化噪声感知泛化挑战，基于域标签的噪声检测仍优于各类域泛化及含噪标签学习方法（包括其组合方案）。在涵盖三种噪声类型的七个异构数据集上实现最高12.5%的性能提升，验证了基于域标签的噪声检测对多样化场景的泛化能力。