Standard empirical risk minimization (ERM) models may prioritize learning
spurious correlations between spurious features and true labels, leading to
poor accuracy on groups where these correlations do not hold. Mitigating this
issue often requires expensive spurious attribute (group) labels or relies on
trained ERM models to infer group labels when group information is unavailable.
However, the significant performance gap in worst-group accuracy between using
pseudo group labels and using oracle group labels inspires us to consider
further improving group robustness through preciser group inference. Therefore,
we propose GIC, a novel method that accurately infers group labels, resulting
in improved worst-group performance. GIC trains a spurious attribute classifier
based on two key properties of spurious correlations: (1) high correlation
between spurious attributes and true labels, and (2) variability in this
correlation between datasets with different group distributions. Empirical
studies on multiple datasets demonstrate the effectiveness of GIC in inferring
group labels, and combining GIC with various downstream invariant learning
methods improves worst-group accuracy, showcasing its powerful flexibility.
Additionally, through analyzing the misclassifications in GIC, we identify an
interesting phenomenon called semantic consistency, which may contribute to
better decoupling the association between spurious attributes and labels,
thereby mitigating spurious correlation.

通过准确推断群体标签，以改善最差群体性能，我们提出了一种新方法 GIC，它通过基于两个关键属性的虚假相关性对其进行分类器训练，多个数据集上的实证研究证明了 GIC 推断群体标签的有效性，并将其与多种下游不变性学习方法结合，提高了最差群体准确性，展示了其强大的灵活性。此外，通过分析 GIC 中的误分类，我们还发现了称为语义一致性的有趣现象，可能有助于解耦虚假属性与标签之间的关联，从而减轻虚假相关性。