  Data augmentation is an effective technique to improve the generalization of
deep neural networks. However, previous data augmentation methods usually treat
the augmented samples equally without considering their individual impacts on
the model. To address this, for the augmented samples from the same training
example, we propose to assign different weights to them. We construct the
maximal expected loss which is the supremum over any reweighted loss on
augmented samples. Inspired by adversarial training, we minimize this maximal
expected loss (MMEL) and obtain a simple and interpretable closed-form
solution: more attention should be paid to augmented samples with large loss
values (i.e., harder examples). Minimizing this maximal expected loss enables
the model to perform well under any reweighting strategy. The proposed method
can generally be applied on top of any data augmentation methods. Experiments
are conducted on both natural language understanding tasks with token-level
data augmentation, and image classification tasks with commonly-used image
augmentation techniques like random crop and horizontal flip. Empirical results
show that the proposed method improves the generalization performance of the
model.
