Network calibration aims to accurately estimate the level of confidences,
which is particularly important for employing deep neural networks in
real-world systems. Recent approaches leverage mixup to calibrate the network's
predictions during training. However, they do not consider the problem that
mixtures of labels in mixup may not accurately represent the actual
distribution of augmented samples. In this paper, we present RankMixup, a novel
mixup-based framework alleviating the problem of the mixture of labels for
network calibration. To this end, we propose to use an ordinal ranking
relationship between raw and mixup-augmented samples as an alternative
supervisory signal to the label mixtures for network calibration. We
hypothesize that the network should estimate a higher level of confidence for
the raw samples than the augmented ones (Fig.1). To implement this idea, we
introduce a mixup-based ranking loss (MRL) that encourages lower confidences
for augmented samples compared to raw ones, maintaining the ranking
relationship. We also propose to leverage the ranking relationship among
multiple mixup-augmented samples to further improve the calibration capability.
Augmented samples with larger mixing coefficients are expected to have higher
confidences and vice versa (Fig.1). That is, the order of confidences should be
aligned with that of mixing coefficients. To this end, we introduce a novel
loss, M-NDCG, in order to reduce the number of misaligned pairs of the
coefficients and confidences. Extensive experimental results on standard
benchmarks for network calibration demonstrate the effectiveness of RankMixup.

RankMixup 是一种基于混合的框架，通过 Ordinal Ranking Relationship 实现网络校准，通过混合系数和置信度之间的顺序关系来提高校准能力，并减少混合标签的问题。

RankMixup: 基于排名的 Mixup 网络校准训练

RankMixup: Ranking-Based Mixup Training for Network Calibration

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.

我们提出了一种新的损失函数，称为 ACLS，可以有效地调整深度神经网络的网络校准问题，同时避免了现有正则化方法的局限性。