  Mixup is a highly successful technique to improve generalization of neural
networks by augmenting the training data with combinations of random pairs.
Selective mixup is a family of methods that apply mixup to specific pairs, e.g.
only combining examples across classes or domains. These methods have claimed
remarkable improvements on benchmarks with distribution shifts, but their
mechanisms and limitations remain poorly understood.
  We examine an overlooked aspect of selective mixup that explains its success
in a completely new light. We find that the non-random selection of pairs
affects the training distribution and improve generalization by means
completely unrelated to the mixing. For example in binary classification, mixup
across classes implicitly resamples the data for a uniform class distribution -
a classical solution to label shift. We show empirically that this implicit
resampling explains much of the improvements in prior work. Theoretically,
these results rely on a regression toward the mean, an accidental property that
we identify in several datasets.
  We have found a new equivalence between two successful methods: selective
mixup and resampling. We identify limits of the former, confirm the
effectiveness of the latter, and find better combinations of their respective
benefits.
