高偏差模型反而更抗分布偏移，提出新方法提升泛化能力
Bias as a Virtue: Rethinking Generalization under Distribution Shifts
- 训练时引入可控统计多样性，让模型主动形成有益偏差
- 实测发现偏差越高，跨分布错误越低，最高降低26.8%误差
- 适合关注鲁棒性、部署后性能的工业级模型开发者
机器学习模型在分布外数据上常表现下降。我们挑战传统验证范式，发现更高的分布内（ID）偏差反而带来更好的分布外（OOD）泛化。所提出的自适应分布桥（ADB）框架通过训练中引入可控统计多样性，使模型具备能跨分布泛化的偏差特征。实验表明，ID偏差与OOD误差间存在稳健负相关，颠覆了以最小化验证误差为核心的常规做法。在多个数据集上，该方法显著提升泛化能力：相比传统交叉验证，平均误差最高降低26.8%，且始终能识别出性能超过74.4百分位的训练策略。本工作既提供实用改进方法，也重构了对偏差在鲁棒学习中作用的理论认知。
原文摘要 · Abstract (English)
Machine learning models often degrade when deployed on data distributions different from their training data. Challenging conventional validation paradigms, we demonstrate that higher in-distribution (ID) bias can lead to better out-of-distribution (OOD) generalization. Our Adaptive Distribution Bridge (ADB) framework implements this insight by introducing controlled statistical diversity during training, enabling models to develop bias profiles that effectively generalize across distributions. Empirically, we observe a robust negative correlation where higher ID bias corresponds to lower OOD error--a finding that contradicts standard practices focused on minimizing validation error. Evaluation on multiple datasets shows our approach significantly improves OOD generalization. ADB achieves robust mean error reductions of up to 26.8% compared to traditional cross-validation, and consistently identifies high-performing training strategies, evidenced by percentile ranks often exceeding 74.4%. Our work provides both a practical method for improving generalization and a theoretical framework for reconsidering the role of bias in robust machine learning.
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