Data augmentation is a technique used to increase the diversity of the training data without collecting more data. It involves applying random transformations to the original data, such as rotating, flipping, cropping, or zooming images.
For instance, in image classification tasks, data augmentation helps prevent overfitting by artificially expanding the dataset, allowing the model to learn more robust features. Augmented data introduces variability, making the model more generalizable to unseen examples.
Data augmentation is particularly valuable when working with limited datasets or when the model needs to handle a variety of real-world conditions that aren't present in the original data.