Which of the following best describes data augmentation?

Data augmentation is fundamentally a technique used to artificially enhance a dataset by generating new data points from existing ones. This is especially relevant in machine learning and deep learning contexts, where large amounts of data are crucial for training effective models. By applying data augmentation methods, such as transformations like rotation, translation, flipping, or adding noise to images, practitioners can effectively create variations of the original data. This leads to a more diverse training dataset and can help improve a model's robustness and performance by reducing overfitting.

In contrast to merely reducing the dataset size or classifying unlabelled data, data augmentation focuses on enriching the dataset, allowing models to generalize better by exposing them to a wider variety of data inputs. While the concept of training AI without human input may relate to unsupervised learning, it does not directly pertain to the process of enhancing datasets through augmentation techniques.