Deep neural networks are increasingly utilized in various machine learning tasks. However, as these models grow in complexity, they often face calibration issues, despite enhanced prediction accuracy. Many studies have endeavored to improve calibration performance through the use of specific loss functions, data preprocessing and training frameworks. Yet, investigations into calibration properties have been somewhat overlooked. Our study leverages the Neural Architecture Search (NAS) search space, offering an exhaustive model architecture space for thorough calibration properties exploration. We specifically create a model calibration dataset. This dataset evaluates 90 bin-based and 12 additional calibration measurements across 117,702 unique neural networks within the widely employed NATS-Bench search space. Our analysis aims to answer several longstanding questions in the field, using our proposed dataset: (i) Can model calibration be generalized across different datasets? (ii) Can robustness be used as a calibration measurement? (iii) How reliable are calibration metrics? (iv) Does a post-hoc calibration method affect all models uniformly? (v) How does calibration interact with accuracy? (vi) What is the impact of bin size on calibration measurement? (vii) Which architectural designs are beneficial for calibration? Additionally, our study bridges an existing gap by exploring calibration within NAS. By providing this dataset, we enable further research into NAS calibration. As far as we are aware, our research represents the first large-scale investigation into calibration properties and the premier study of calibration issues within NAS.

翻译：深度神经网络正日益广泛应用于各类机器学习任务。然而，随着模型复杂度提升，尽管预测精度提高，它们常面临校准问题。许多研究通过采用特定损失函数、数据预处理和训练框架来改进校准性能，但针对校准特性的研究却相对匮乏。本研究利用神经架构搜索（NAS）搜索空间，提供了详尽的模型架构空间以深入探索校准特性。我们特别构建了一个模型校准数据集。该数据集在广泛使用的NATS-Bench搜索空间中，对117,702个独特神经网络的90种基于分箱（bin-based）的校准度量及12种额外校准度量进行评估。基于所提出的数据集，我们的分析旨在回答领域内若干长期未决问题：（i）模型校准能否跨不同数据集泛化？（ii）鲁棒性能否用作校准度量？（iii）校准指标的可信度如何？（iv）事后（post-hoc）校准方法是否对所有模型产生一致影响？（v）校准与精度如何交互作用？（vi）分箱大小对校准度量有何影响？（vii）哪些架构设计有益于校准？此外，本研究填补了NAS中校准研究的空白。通过提供该数据集，我们推动了NAS校准领域的进一步研究。据我们所知，本研究是首次关于校准特性的大规模调查，也是NAS校准问题的开创性研究。