  Data augmentation is arguably the most important regularization technique
commonly used to improve generalization performance of machine learning models.
It primarily involves the application of appropriate data transformation
operations to create new data samples with desired properties. Despite its
effectiveness, the process is often challenging because of the time-consuming
trial and error procedures for creating and testing different candidate
augmentations and their hyperparameters manually. Automated data augmentation
methods aim to automate the process. State-of-the-art approaches typically rely
on automated machine learning (AutoML) principles. This work presents a
comprehensive survey of AutoML-based data augmentation techniques. We discuss
various approaches for accomplishing data augmentation with AutoML, including
data manipulation, data integration and data synthesis techniques. We present
extensive discussion of techniques for realizing each of the major subtasks of
the data augmentation process: search space design, hyperparameter optimization
and model evaluation. Finally, we carried out an extensive comparison and
analysis of the performance of automated data augmentation techniques and
state-of-the-art methods based on classical augmentation approaches. The
results show that AutoML methods for data augmentation currently outperform
state-of-the-art techniques based on conventional approaches.
