  Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep
neural networks where additional samples are generated during training by
convexly combining random pairs of images and their associated labels. While
simple to implement, it has been shown to be a surprisingly effective method of
data augmentation for image classification: DNNs trained with mixup show
noticeable gains in classification performance on a number of image
classification benchmarks. In this work, we discuss a hitherto untouched aspect
of mixup training -- the calibration and predictive uncertainty of models
trained with mixup. We find that DNNs trained with mixup are significantly
better calibrated -- i.e., the predicted softmax scores are much better
indicators of the actual likelihood of a correct prediction -- than DNNs
trained in the regular fashion. We conduct experiments on a number of image
classification architectures and datasets -- including large-scale datasets
like ImageNet -- and find this to be the case. Additionally, we find that
merely mixing features does not result in the same calibration benefit and that
the label smoothing in mixup training plays a significant role in improving
calibration. Finally, we also observe that mixup-trained DNNs are less prone to
over-confident predictions on out-of-distribution and random-noise data. We
conclude that the typical overconfidence seen in neural networks, even on
in-distribution data is likely a consequence of training with hard labels,
suggesting that mixup be employed for classification tasks where predictive
uncertainty is a significant concern.
