  Recent work has shown that data augmentation has the potential to
significantly improve the generalization of deep learning models. Recently,
automated augmentation strategies have led to state-of-the-art results in image
classification and object detection. While these strategies were optimized for
improving validation accuracy, they also led to state-of-the-art results in
semi-supervised learning and improved robustness to common corruptions of
images. An obstacle to a large-scale adoption of these methods is a separate
search phase which increases the training complexity and may substantially
increase the computational cost. Additionally, due to the separate search
phase, these approaches are unable to adjust the regularization strength based
on model or dataset size. Automated augmentation policies are often found by
training small models on small datasets and subsequently applied to train
larger models. In this work, we remove both of these obstacles. RandAugment has
a significantly reduced search space which allows it to be trained on the
target task with no need for a separate proxy task. Furthermore, due to the
parameterization, the regularization strength may be tailored to different
model and dataset sizes. RandAugment can be used uniformly across different
tasks and datasets and works out of the box, matching or surpassing all
previous automated augmentation approaches on CIFAR-10/100, SVHN, and ImageNet.
On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the
previous state-of-the-art and 1.0% increase over baseline augmentation. On
object detection, RandAugment leads to 1.0-1.3% improvement over baseline
augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to
its interpretable hyperparameter, RandAugment may be used to investigate the
role of data augmentation with varying model and dataset size. Code is
available online.
