Data augmentation is the process of using computer algorthms or other synthetic means to increase the size of a collected dataset.

Machine learning algorithms typically become more resistant to overfitting when they are trained with more data. But in many cases, it can be expensive to collect more data.

It is often possible to significantly increase the size of a dataset by computing simple transformations that are unlikely to be learned by the model, but also do not change the value of the labels.

For example, if one were building a model to identify pictures of cats versus non-cats, flipping the pictures horizontally or vertically increases the size of the available image data. When the pictures are flipped, whether they contain a cat or not doesn’t change. Despite this, many statistical models would be unlikely to learn flip-invariance without a significant amount of data–either collected from the real-world or generated through data augmentation.