Tailoring Mixup to Data for Calibration
Quentin Bouniot Pavlo Mozharovskyi Florence d'Alché-Buc
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. 
  Along with improved predictive performance, Mixup is also a good technique for improving calibration.
  However, mixing data carelessly can lead to manifold mismatch, i.e., synthetic data lying outside original  class manifolds, which can deteriorate calibration.
  In this work, we show that the likelihood of assigning a wrong label with mixup increases with the distance between data to mix. 
  To this end, we propose to dynamically change the underlying distributions of interpolation coefficients 
  depending on the similarity between samples to mix, and define a flexible framework to do so without losing in diversity. We provide extensive experiments for classification and regression tasks, showing that our proposed method improves predictive performance 
  and calibration of models, while being much more efficient.
mixup, calibration, confidence, robustness
We show that taking distance into account in mixup reduces occurence of mismatch between mixed labels and mixed samples, improving confidence, calibration and robustness.