Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification

• URL: http://arxiv.org/abs/2505.06831v1
• Date: Sun, 11 May 2025 04:01:34 GMT
• Title: Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification
• Authors: Miaoyun Zhao, Qiang Zhang, Chenrong Li,
• Abstract summary: Bias Exploration via Overfitting (BEO) captures each distribution in greater detail by modeling it as a mixture of latent groups.<n>We introduce a fine-grained variant of CCDB, termed FG-CCDB, which performs more precise distribution matching and balancing within each group.<n>Our method performs on par with bias-supervised approaches on binary classification tasks and significantly outperforms them in highly biased multi-class scenarios.
• Score: 11.525201208566925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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