Concept-Residual Representation Expansion for Robustness to Spurious Correlations

作者：Eric Xie, Guangzhi Xiong, Wenqian Ye, Aidong Zhang · 年份：2026 · DOI：10.1145/3770855.3818089 · 研究领域：Computer science、Artificial intelligence、Machine learning、Algorithm、Data mining、Mathematics

Models trained with empirical risk minimization (ERM) are prone to relying on spurious correlations to make predictions. A spurious correlation is a non-causal relationship in the training data between an attribute and the prediction target that does not generalize beyond the training environment. As a result, models can appear to achieve strong performance by exploiting these correlations, yet fail when the correlation changes or disappears. Despite their tendency to learn spurious correlations, the success of post-hoc mitigation methods in recent work suggests that ERM-trained models still retain useful, robust predictive features. However, core (non-spurious) features may be weak or entangled within the representation, making them difficult to identify. We propose Concept-Residual eXpansion (CRX), a concept-augmented framework that improves robustness by expanding the set of candidate predictive features. Starting from a frozen ERM representation, we augment the model's features with interpretable concept scores that describe the presence of task-relevant attributes and the surrounding context, together with residual features that capture the portion of the ERM features not expressed by the concepts. We then retrain a lightweight classifier on this expanded feature space, enabling it to leverage both structured semantic cues and complementary residual information. Across standard spurious correlation benchmarks, CRX consistently improves worst-group accuracy while maintain...