  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.
