We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based method that exploits a regularization term to smooth the miscalibrated confidences. Although these approaches have shown the effectiveness on calibrating the networks, there is still a lack of understanding on the underlying principles of regularization in terms of network calibration. We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration. Specifically, we have observed that 1) the regularization-based methods can be interpreted as variants of label smoothing, and 2) they do not always behave desirably. Based on the analysis, we introduce a novel loss function, dubbed ACLS, that unifies the merits of existing regularization methods, while avoiding the limitations. We show extensive experimental results for image classification and semantic segmentation on standard benchmarks, including CIFAR10, Tiny-ImageNet, ImageNet, and PASCAL VOC, demonstrating the effectiveness of our loss function.

翻译：我们研究了网络校准问题，即调整深度神经网络中的错误置信度。许多网络校准方法采用基于正则化的技术，利用正则化项平滑错误置信度。尽管这些方法在校准网络上展现了有效性，但在正则化对网络校准影响的基本原理方面仍缺乏理解。本文对现有基于正则化的方法进行了深入分析，以更好地理解它们如何影响网络校准。具体而言，我们观察到：1）这些正则化方法可被解释为标签平滑的变体；2）它们并非始终表现出理想行为。基于此分析，我们提出了一种新型损失函数，命名为ACLS，它统一了现有正则化方法的优点，同时避免了其局限性。我们在标准基准数据集（包括CIFAR10、Tiny-ImageNet、ImageNet和PASCAL VOC）上展示了图像分类和语义分割的大量实验结果，证明了我们损失函数的有效性。