What is Data Augmentation?

Data augmentation is a set of techniques used to artificially increase the amount of training data by creating modified versions of existing data. This is done to improve the performance and generalization ability of machine learning models, especially when the available dataset is limited. Common data augmentation techniques include image rotations, flips, crops, zooms, adding noise, and color adjustments for images; and adding noise, time shifting, and pitch shifting for audio.