  Mixup is a recent regularizer for current deep classification networks.
Through training a neural network on convex combinations of pairs of examples
and their labels, it imposes locally linear constraints on the model's input
space. However, such strict linear constraints often lead to under-fitting
which degrades the effects of regularization. Noticeably, this issue is getting
more serious when the resource is extremely limited. To address these issues,
we propose the Adversarial Mixing Policy (AMP), organized in a min-max-rand
formulation, to relax the Locally Linear Constraints in Mixup. Specifically,
AMP adds a small adversarial perturbation to the mixing coefficients rather
than the examples. Thus, slight non-linearity is injected in-between the
synthetic examples and synthetic labels. By training on these data, the deep
networks are further regularized, and thus achieve a lower predictive error
rate. Experiments on five text classification benchmarks and five backbone
models have empirically shown that our methods reduce the error rate over Mixup
variants in a significant margin (up to 31.3%), especially in low-resource
conditions (up to 17.5%).
