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

Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup