# Safe Augmentation: Learning Task-Specific Transformations from Data

## TL;DR

Safe Augmentation is a simple, explainable method that learns task-specific data transformations to improve deep learning model generalization without altering data distribution, outperforming manual augmentation techniques.

## Contribution

The paper introduces Safe Augmentation, a novel approach to automatically learn task-specific, distribution-preserving data transformations for better model generalization.

## Key findings

- Improves accuracy on multiple datasets including CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and Cityscapes.
- Model-agnostic and easy to implement.
- Outperforms baseline augmentation methods.

## Abstract

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, augmentations are dataset-specific, and the optimal augmentations set on a specific dataset has limited transferability to others. We present a simple and explainable method called $\textbf{Safe Augmentation}$ that can learn task-specific data augmentation techniques that do not change the data distribution and improve the generalization of the model. We propose to use safe augmentation in two ways: for model fine-tuning and along with other augmentation techniques. Our method is model-agnostic, easy to implement, and achieves better accuracy on CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and Cityscapes datasets comparing to baseline augmentation techniques. The code is available at $\href{https://github.com/Irynei/SafeAugmentation}{https://github.com/Irynei/SafeAugmentation}$.