RandAugment: Practical automated data augmentation with a reduced search space

Understand

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.