对如何发现虚假相关性、捷径学习、聪明汉效应或群体分布非鲁棒性及如何修复它们的可重复性研究
Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them
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中文总结 AI 辅助
本文通过比较分析探讨了在数据有限和子群不平衡等约束下，XAI方法在修复模型失败中的优势，发现CFKD在提升泛化能力上表现最佳，但许多方法依赖群体标签导致实际应用受限。
中文摘要 AI 辅助
深度神经网络（DNN）在医疗诊断和自动驾驶等高风险领域中被日益广泛使用，但确保其可靠性研究在不同社区中术语碎片化。尽管分布鲁棒优化（DRO）、不变风险最小化（IRM）、捷径学习、简单性偏差和聪明汉效应等框架均针对虚假相关性导致的模型失败，但研究人员通常只参考自身领域的工作。本可重复性研究通过在数据有限和严重子群不平衡等挑战性约束下比较修正方法，统一了这些视角。我们评估了基于可解释人工智能（XAI）技术的最新修正方法与流行非XAI基线方法，使用合成和现实数据集。研究发现，XAI方法通常优于非XAI方法，其中反事实知识蒸馏（CFKD）在提升泛化能力上最为一致有效。此外，许多方法的实际应用受到依赖群体标签的限制，因为手动标注往往不可行，而自动化工具如频谱相关性分析（SpRAy）在处理复杂特征和严重不平衡时表现不佳。此外，验证集中少数群体样本的稀缺性使模型选择和超参数调优不可靠，这成为在安全关键领域部署稳健和可信模型的重大障碍。
英文摘要
Deep Neural Networks (DNNs) are increasingly utilized in high-stakes domains like medical diagnostics and autonomous driving where model reliability is critical. However, the research landscape for ensuring this reliability is terminologically fractured across communities that pursue the same goal of ensuring models rely on causally relevant features rather than confounding signals. While frameworks such as distributionally robust optimization (DRO), invariant risk minimization (IRM), shortcut learning, simplicity bias, and the Clever Hans effect all address model failure due to spurious correlations, researchers typically only reference work within their own domains. This reproducibility study unifies these perspectives through a comparative analysis of correction methods under challenging constraints like limited data availability and severe subgroup imbalance. We evaluate recently proposed correction methods based on explainable artificial intelligence (XAI) techniques alongside popular non-XAI baselines using both synthetic and real-world datasets. Findings show that XAI-based methods generally outperform non-XAI approaches, with Counterfactual Knowledge Distillation (CFKD) proving most consistently effective at improving generalization. Our experiments also reveal that the practical application of many methods is hindered by a dependency on group labels, as manual annotation is often infeasible and automated tools like Spectral Relevance Analysis (SpRAy) struggle with complex features and severe imbalance. Furthermore, the scarcity of minority group samples in validation sets renders model selection and hyperparameter tuning unreliable, posing a significant obstacle to the deployment of robust and trustworthy models in safety-critical areas.
发表机构
- Technische Universität Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。
