In this paper we propose $epsilon$-Consistent Mixup ($epsilon$mu). $epsilon$mu is a data-based structural regularization technique that combines Mixups 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 $epsilon$mus effectiveness. In particular, $epsilon$mu is found to produce more accurate synthetic labels and more confident predictions than Mixup.
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