Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real world settings, the subpopulations most affected by such disparities are frequently unlabeled or unknown, thereby motivating the development of methods that are performant on sensitive subgroups without being pre-specified. However, existing group-robust methods typically assume prior knowledge of relevant subgroups, using group annotations for training or model selection. We propose Low-rank Error Informed Adaptation (LEIA), a simple two-stage method that improves group robustness by identifying a low-dimensional subspace in the representation space where model errors concentrate. LEIA restricts adaptation to this error-informed subspace via a low-rank adjustment to the classifier logits, directly targeting latent failure modes without modifying the backbone or requiring group labels. Using five real-world datasets, we analyze group robustness under three settings: (1) truly no knowledge of subgroup relevance, (2) partial knowledge of subgroup relevance, and (3) full knowledge of subgroup relevance. Across all settings, LEIA consistently improves worst-group performance while remaining fast, parameter-efficient, and robust to hyperparameter choice.

翻译：为优化平均准确率而训练的深度学习模型，往往在特定子群体上表现出系统性失效。在现实场景中，受此类差异影响最大的子群体通常缺乏标注或处于未知状态，这推动了相关方法的发展，旨在无需预先指定敏感子群体的前提下，确保模型在其上的性能表现。然而，现有的群体鲁棒性方法通常假设已知相关子群体，并依赖群体标注进行训练或模型选择。本文提出低秩误差信息适应方法，这是一种简单的两阶段方法，通过在表征空间中识别模型误差集中的低维子空间来提升群体鲁棒性。该方法通过低秩调整分类器对数概率，将适应过程限制在这一误差信息子空间内，直接针对潜在的失效模式，而无需修改主干网络或依赖群体标签。基于五个真实世界数据集，我们在三种设定下分析群体鲁棒性：（1）完全未知子群体相关性；（2）部分已知子群体相关性；（3）完全已知子群体相关性。在所有设定中，该方法均能持续提升最差群体性能，同时保持快速、参数高效且对超参数选择具有鲁棒性。