Models trained with empirical risk minimization (ERM) are known to learn to rely on spurious features, i.e., their prediction is based on undesired auxiliary features which are strongly correlated with class labels but lack causal reasoning. This behavior particularly degrades accuracy in groups of samples of the correlated class that are missing the spurious feature or samples of the opposite class but with the spurious feature present. The recently proposed Deep Feature Reweighting (DFR) method improves accuracy of these worst groups. Based on the main argument that ERM mods can learn core features sufficiently well, DFR only needs to retrain the last layer of the classification model with a small group-balanced data set. In this work, we examine the applicability of DFR to realistic data in the medical domain. Furthermore, we investigate the reasoning behind the effectiveness of last-layer retraining and show that even though DFR has the potential to improve the accuracy of the worst group, it remains susceptible to spurious correlations.

翻译：经验风险最小化（ERM）训练的模型已知会依赖虚假特征，即其预测基于与类别标签强相关但缺乏因果逻辑的非理想辅助特征。这种行为尤其会降低那些缺失虚假特征的相关类别样本，或包含虚假特征的反类别样本的群体准确性。近期提出的深度特征重加权（DFR）方法能提升这些最差群体的准确性。基于ERM模型可充分学习核心特征的核心论点，DFR仅需使用小型群体平衡数据集重训练分类模型的最后一层。本研究考察了DFR在医学领域真实数据中的适用性。此外，我们探究了最后一层重训练有效性的内在机理，并表明即使DFR具备提升最差群体准确性的潜力，其仍易受虚假相关性影响。