Data augmentation is a technique used to enhance the diversity of a training dataset by applying various transformations to the existing data. This is particularly useful in machine learning and deep learning, as it allows models to learn from a larger variety of examples without the need to collect additional data. By transforming images in ways that preserve their class labels, models can become more robust and better at generalizing from the training data.

Common transformations used in data augmentation include image rotation, flipping, scaling, and color adjustments. These transformations help the model to learn to recognize objects under different conditions, such as varying orientations or lighting. For instance, a model trained with augmented data can better identify a car regardless of whether it appears facing left or right in an image ¹⁵.

Overall, data augmentation is a valuable strategy for improving model performance, especially when the available training data is limited. It allows for the creation of a more comprehensive dataset, which can lead to more reliable and accurate models ²⁴.