Safe Augmentation: Learning Task-Specific Transformations from Data

Data augmentation is widely used as a part of the training process applied to deep learning models, especially in the computer vision domain. Currently, common data augmentation techniques are designed manually. Therefore they require expert knowledge and time. Moreover, optimal augmentations found for one dataset, often do not transfer to other datasets as effectively. We propose a simple novel method that can automatically learn task-specific data augmentation techniques called safe augmentations that do not break the data distribution and can be used to improve model performance. Moreover, we provided a new training pipeline for using safe augmentations for different computer vision tasks. Our method works both with image classification and image segmentation and achieves significantly better accuracy on CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet and Cityscapes datasets comparing to other augmentation techniques