SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations

2026-09-01

AI Summary

为解决数据集中虚假关联导致的模型对少数群体表现不佳的问题，提出SAGE方法，通过生成增强来平衡训练集并提高最差组准确率。

Abstract

Spurious correlations pose a significant challenge to the robustness of modern machine learning. The inherent imbalance in dataset distributions often leads traditional Empirical Risk Minimization (ERM) models to rely on majority spurious attributes for classification, resulting in poor performance on minority groups. This problem becomes particularly challenging when the spurious attributes are unavailable. Existing group-label-free methods often upsample minority groups or misclassified real training examples; repeating the same instances can reduce effective diversity and encourage overfitting. To mitigate these spurious correlations from a data-centric perspective in the absence of prior knowledge, we introduce Subpopulation-Aware Generative Enhancement (SAGE), a two-stage generative augmentation framework. Using cluster-derived sub-labels and class labels, we fine-tune a conditional generative model and text encoder, generating targeted synthetic data to fill underrepresented regions in the training set and construct a balanced validation set for last-layer reweighting. We experimentally show that SAGE achieves 89.5%, 85.7%, and 79.1% worst-group accuracy on Waterbirds, CelebA, and MetaShift, respectively, outperforming the best group-label-free baselines by up to 7.7 percentage points.

Problem

• spurious correlations
• imbalance in dataset distributions
• Empirical Risk Minimization (ERM)
• minority groups
• generative augmentation

Innovation

• Subpopulation-Aware
• Generative Enhancement
• Spurious Correlations
• Data Augmentation