  Deep Neural Networks (DNN) represent the state of the art in many tasks.
However, due to their overparameterization, their generalization capabilities
are in doubt and still a field under study. Consequently, DNN can overfit and
assign overconfident predictions -- effects that have been shown to affect the
calibration of the confidences assigned to unseen data. Data Augmentation (DA)
strategies have been proposed to regularize these models, being Mixup one of
the most popular due to its ability to improve the accuracy, the uncertainty
quantification and the calibration of DNN. In this work however we argue and
provide empirical evidence that, due to its fundamentals, Mixup does not
necessarily improve calibration. Based on our observations we propose a new
loss function that improves the calibration, and also sometimes the accuracy,
of DNN trained with this DA technique. Our loss is inspired by Bayes decision
theory and introduces a new training framework for designing losses for
probabilistic modelling. We provide state-of-the-art accuracy with consistent
improvements in calibration performance. Appendix and code are provided here:
https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch.git
