  In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
$\epsilon$mu is a data-based structural regularization technique that combines
Mixup's linear interpolation with consistency regularization in the Mixup
direction, by compelling a simple adaptive tradeoff between the two. This
learnable combination of consistency and interpolation induces a more flexible
structure on the evolution of the response across the feature space and is
shown to improve semi-supervised classification accuracy on the SVHN and
CIFAR10 benchmark datasets, yielding the largest gains in the most challenging
low label-availability scenarios. Empirical studies comparing $\epsilon$mu and
Mixup are presented and provide insight into the mechanisms behind
$\epsilon$mu's effectiveness. In particular, $\epsilon$mu is found to produce
more accurate synthetic labels and more confident predictions than Mixup.
