# On Calibration of Mixup Training for Deep Neural Networks

arXiv: https://arxiv.org/abs/2003.09946 · HTML: https://mlsanity.com/paper/2003.09946/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration](https://mlsanity.com/paper/2203.13834/) (2022)
- [Towards Improving Calibration in Object Detection Under Domain Shift](https://mlsanity.com/paper/2209.07601/) (2022)
- [Beyond One-Hot Labels: Semantic Mixing for Model Calibration](https://mlsanity.com/paper/2504.13548/) (2025)
- [h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective](https://mlsanity.com/paper/2506.17968/) (2025)
- [SAMix: Calibrated and Accurate Continual Learning via Sphere-Adaptive Mixup and Neural Collapse](https://mlsanity.com/paper/2510.15751/) (2025)
- [Impact of Data Augmentation on Confidence Calibration in Melanoma Classification](https://mlsanity.com/paper/2610.06146/) (2026)

## Abstract (arXiv)

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