What does data augmentation achieve in AI?

Data augmentation plays a crucial role in enhancing the performance of machine learning models, particularly in the field of computer vision and natural language processing. By modifying existing data, data augmentation effectively increases the size of the training dataset without the need to collect additional data points. This process can involve various techniques, such as applying transformations like rotation, scaling, cropping, or adding noise to images, as well as paraphrasing or changing the tone of sentences in text data.

The main benefit of data augmentation is that it helps improve the robustness and generalization of the model by allowing it to learn from a wider variety of inputs, reducing the risk of overfitting to the specific examples in the original dataset. By providing the model with diverse variations of the same underlying data, it becomes better equipped to handle unseen data during the evaluation phase. Thus, increasing the training data by modifying existing data is the essence of what data augmentation achieves.