ICLR 2025

Published as a conference paper at ICLR 2025
TAILORING MIXUP TO DATA FOR CALIBRATION
Quentin Bouniot,1,2,3,4 Pavlo Mozharovskyi1 & Florence d’Alché-Buc1
1LTCI, Télécom Paris, Institut Polytechnique de Paris, France,
2Technical University of Munich,
3Helmholtz Munich,
4Munich Center for Machine Learning (MCML)
ABSTRACT
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.
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INTRODUCTION
The Vicinal Risk Minimization (VRM) principle (Chapelle et al., 2000) improves on the well-known
Empirical Risk Minimization (ERM) (Vapnik, 1998) for training deep neural networks by drawing
virtual samples from a vicinity around true training data. This data augmentation principle is known to
improve the generalization ability of deep neural networks when the number of observed data is small
compared to the task complexity. In practice, the method of choice to implement it relies on hand-
crafted procedures to mimic natural perturbations (Yaeger et al., 1996; Ha & Bunke, 1997; Simard
et al., 2002). However, one counterintuitive but effective and less application...