Despite the rapid development of machine learning algorithms for domain generalization (DG), there is no clear empirical evidence that the existing DG algorithms outperform the classic empirical risk minimization (ERM) across standard benchmarks. To better understand this phenomenon, we investigate whether there are benefits of DG algorithms over ERM through the lens of label noise. Specifically, our finite-sample analysis reveals that label noise exacerbates the effect of spurious correlations for ERM, undermining generalization. Conversely, we illustrate that DG algorithms exhibit implicit label-noise robustness during finite-sample training even when spurious correlation is present. Such desirable property helps mitigate spurious correlations and improve generalization in synthetic experiments. However, additional comprehensive experiments on real-world benchmark datasets indicate that label-noise robustness does not necessarily translate to better performance compared to ERM. We conjecture that the failure mode of ERM arising from spurious correlations may be less pronounced in practice.

机器学习算法中的领域泛化与经验风险最小化的效果在标准基准测试中没有明确的经验证据，但是通过标签噪声的研究可知，领域泛化算法在有限样本训练中具有隐含的标签噪声鲁棒性，在合成实验中可以缓解虚假相关性并提高泛化性能，但在真实世界的基准数据集上的综合实验表明，标签噪声鲁棒性并不能比经验风险最小化算法取得更好的性能。我们推测，由虚假相关性引起的经验风险最小化的失败模式在实践中可能不明显。

理解领域泛化：噪声稳健性视角