Data augmentation is used to achieve what in machine learning?

Data augmentation is a critical technique in machine learning, particularly in the context of training models for tasks involving images, text, or other data types. Its primary goal is to enhance model robustness. This is achieved by artificially increasing the size and diversity of the training dataset.

When models are exposed to a wider variety of training examples—through transformations such as rotation, scaling, cropping for images, or synonym replacement in text—they become better at generalizing to unseen data. This means that the model can learn to recognize patterns and features more effectively, leading to improved performance when encountering real-world data that may differ from the original training set.

By increasing the training varietal, data augmentation helps prevent overfitting, where a model learns to perform exceptionally well on the training data but fails to generalize to new instances. Thus, through techniques of data augmentation, the resilience and accuracy of models are significantly enhanced, leading to better overall performance in real-world applications.