  Data augmentation methods enrich datasets with augmented data to improve the
performance of neural networks. Recently, automated data augmentation methods
have emerged, which automatically design augmentation strategies. Existing work
focuses on image classification and object detection, whereas we provide the
first study on semantic image segmentation and introduce two new approaches:
\textit{SmartAugment} and \textit{SmartSamplingAugment}. SmartAugment uses
Bayesian Optimization to search over a rich space of augmentation strategies
and achieves a new state-of-the-art performance in all semantic segmentation
tasks we consider. SmartSamplingAugment, a simple parameter-free approach with
a fixed augmentation strategy competes in performance with the existing
resource-intensive approaches and outperforms cheap state-of-the-art data
augmentation methods. Further, we analyze the impact, interaction, and
importance of data augmentation hyperparameters and perform ablation studies,
which confirm our design choices behind SmartAugment and SmartSamplingAugment.
Lastly, we will provide our source code for reproducibility and to facilitate
further research.
