Recent advances in deep learning models have been largely attributed to the quantity and diversity of data gathered in recent years. Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks for image analysis, and in more recent years adaptive techniques, such as GAN-based or Model-based approaches, have been proposed to increase the effectiveness of data augmentation strategies. Different data augmentation strategies are likely to perform differently depending on the type of input and visual task.