Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extrapolation capabilities. In this paper, we systematically explore anchoring as a general protocol for training vision models, providing fundamental insights into its training and inference processes and their implications for generalization and safety. Despite its promise, we identify a critical problem in anchored training that can lead to an increased risk of learning undesirable shortcuts, thereby limiting its generalization capabilities. To address this, we introduce a new anchored training protocol that employs a simple regularizer to mitigate this issue and significantly enhances generalization. We empirically evaluate our proposed approach across datasets and architectures of varying scales and complexities, demonstrating substantial performance gains in generalization and safety metrics compared to the standard training protocol.

通过研究锚定方法作为训练视觉模型的通用协议，揭示了其训练和推断过程对泛化和安全性的影响，同时提出了一种新的锚定训练协议以解决其存在的问题，并在数据集和架构的不同规模和复杂性上进行了实证评估，显示出在泛化和安全性指标方面与标准训练协议相比的显著性能提升。

关于使用锚定训练视觉模型