  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 performance, Mixup is also a good
technique for improving calibration and predictive uncertainty. However, mixing
data carelessly can lead to manifold intrusion, i.e., conflicts between the
synthetic labels assigned and the true label distributions, which can
deteriorate calibration. In this work, we argue that the likelihood of manifold
intrusion 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 performance and calibration of models, while being much more
efficient. The code for our work is available at
https://github.com/qbouniot/sim_kernel_mixup.
