Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification
This addresses the problem of spurious correlations in machine learning for researchers and practitioners, offering an incremental improvement over existing bias-agnostic methods.
The paper tackles the challenge of group-robust generalization in classification when bias annotations are unavailable, by proposing a method that models distributions as mixtures of latent groups and performs fine-grained distribution matching, achieving performance on par with bias-supervised methods in binary tasks and significantly outperforming them in multi-class scenarios.
Achieving group-robust generalization in the presence of spurious correlations remains a significant challenge, particularly when bias annotations are unavailable. Recent studies on Class-Conditional Distribution Balancing (CCDB) reveal that spurious correlations often stem from mismatches between the class-conditional and marginal distributions of bias attributes. They achieve promising results by addressing this issue through simple distribution matching in a bias-agnostic manner. However, CCDB approximates each distribution using a single Gaussian, which is overly simplistic and rarely holds in real-world applications. To address this limitation, we propose a novel method called Bias Exploration via Overfitting (BEO), which captures each distribution in greater detail by modeling it as a mixture of latent groups. Building on these group-level descriptions, we introduce a fine-grained variant of CCDB, termed FG-CCDB, which performs more precise distribution matching and balancing within each group. Through group-level reweighting, FG-CCDB learns sample weights from a global perspective, achieving stronger mitigation of spurious correlations without incurring substantial storage or computational costs. Extensive experiments demonstrate that BEO serves as a strong proxy for ground-truth bias annotations and can be seamlessly integrated with bias-supervised methods. Moreover, when combined with FG-CCDB, our method performs on par with bias-supervised approaches on binary classification tasks and significantly outperforms them in highly biased multi-class scenarios.