Enhancing Robust Fairness via Confusional Spectral Regularization

• URL: http://arxiv.org/abs/2501.13273v1
• Date: Wed, 22 Jan 2025 23:32:19 GMT
• Title: Enhancing Robust Fairness via Confusional Spectral Regularization
• Authors: Gaojie Jin, Sihao Wu, Jiaxu Liu, Tianjin Huang, Ronghui Mu,
• Abstract summary: We derive a robust generalization bound for the worst-class robust error within the PAC-Bayesian framework. We propose a novel regularization technique to improve worst-class robust accuracy and enhance robust fairness.
• Score: 6.041034366572273
• License:
• Abstract: Recent research has highlighted a critical issue known as ``robust fairness", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to those with lower empirical robust performance. However, we find there is a divergence of class-wise robust performance between training set and testing set, which limits the effectiveness of these explicit reweighting methods, indicating the need for a principled alternative. In this work, we derive a robust generalization bound for the worst-class robust error within the PAC-Bayesian framework, accounting for unknown data distributions. Our analysis shows that the worst-class robust error is influenced by two main factors: the spectral norm of the empirical robust confusion matrix and the information embedded in the model and training set. While the latter has been extensively studied, we propose a novel regularization technique targeting the spectral norm of the robust confusion matrix to improve worst-class robust accuracy and enhance robust fairness. We validate our approach through comprehensive experiments on various datasets and models, demonstrating its effectiveness in enhancing robust fairness.

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