What is "data augmentation" in relation to generative AI?

Data augmentation refers to techniques that are used to artificially expand a training dataset by creating modified versions of existing data points. This is particularly valuable in the context of machine learning and generative AI, where having a larger dataset can help improve the performance and robustness of models.

For example, in image processing, data augmentation techniques might involve rotating images, flipping them, adjusting brightness, or adding noise. These variations help the model generalize better by exposing it to a wider range of input scenarios, even though the same underlying data point is being altered in different ways.

The ability to generate additional training examples through augmentation is crucial in domains where obtaining real data is expensive, time-consuming, or impractical. By effectively increasing the diversity and size of the training dataset, data augmentation contributes significantly to the model's ability to learn and perform effectively on unseen data.