Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which is characterized by learning everything presented in the training set, resulting in overconfident predictions during testing. Existing methods typically address overfitting and mitigate the miscalibration by adding a maximum-entropy regularizer to the objective function. The objective can be understood as seeking a model that fits the ground-truth labels by increasing the confidence while also maximizing the entropy of predicted probabilities by decreasing the confidence. However, previous methods lack clear guidance on confidence adjustment, leading to conflicting objectives (increasing but also decreasing confidence). Therefore, we introduce a method called Dynamic Regularization (DReg), which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off. At a high level, DReg aims to obtain a more reliable model capable of acknowledging what it knows and does not know. Specifically, DReg effectively fits the labels for in-distribution samples (samples that should be learned) while applying regularization dynamically to samples beyond model capabilities (e.g., outliers), thereby obtaining a robust calibrated model especially on the samples beyond model capabilities. Both theoretical and empirical analyses sufficiently demonstrate the superiority of DReg compared with previous methods.

翻译：深度学习中的校准偏差是指预测置信度与模型实际性能之间存在差异。该问题通常源于过拟合现象——模型过度学习训练集中的全部特征，导致测试时产生过度自信的预测。现有方法通常通过在目标函数中引入最大熵正则化项来缓解过拟合与校准偏差，其目标可理解为：通过提升置信度拟合真实标签，同时通过降低置信度最大化预测概率的熵。然而，这类方法缺乏置信度调整的明确指引，导致增加与降低置信度之间存在目标冲突。为此，我们提出动态正则化（DReg）方法，旨在让模型在训练过程中自主学习应当学习的特征，从而规避置信度调整的权衡困境。在高层次上，DReg致力于构建更可靠的模型，使其能够识别自身已知与未知的领域。具体而言，DReg在有效拟合分布内样本（应被学习的样本）标签的同时，对超出模型能力范围的样本（如异常值）动态施加正则化约束，从而在尤其是超出模型能力范围的样本上获得鲁棒的校准模型。理论与实证分析充分证明了DReg相较于现有方法的优越性。