Data augmentation is a key practice in machine learning for improving
generalization performance. However, finding the best data augmentation
hyperparameters requires domain knowledge or a computationally demanding
search. We address this issue by proposing an efficient approach to
automatically train a network that learns an effective distribution of
transformations to improve its generalization. Using bilevel optimization, we
directly optimize the data augmentation parameters using a validation set. This
framework can be used as a general solution to learn the optimal data
augmentation jointly with an end task model like a classifier. Results show
that our joint training method produces an image classification accuracy that
is comparable to or better than carefully hand-crafted data augmentation. Yet,
it does not need an expensive external validation loop on the data augmentation
hyperparameters.

本研究提出了一种用双层优化训练网络的方法，以自动学习有效的数据增强转换的分布，从而提高其泛化性能并与分类器等模型联合训练，实现数据增强的最优化，相较于手工优化方式，本方法在图像分类准确性上表现更好同时无需昂贵的数据增强超参数外部验证循环。