  Mixup is a data-dependent regularization technique that consists in linearly
interpolating input samples and associated outputs. It has been shown to
improve accuracy when used to train on standard machine learning datasets.
However, authors have pointed out that Mixup can produce out-of-distribution
virtual samples and even contradictions in the augmented training set,
potentially resulting in adversarial effects. In this paper, we introduce Local
Mixup in which distant input samples are weighted down when computing the loss.
In constrained settings we demonstrate that Local Mixup can create a trade-off
between bias and variance, with the extreme cases reducing to vanilla training
and classical Mixup. Using standardized computer vision benchmarks , we also
show that Local Mixup can improve test accuracy.
