  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.
