What does “Data Augmentation” mean?

How well a model performs on new, unseen data outside the training set.

When benchmark test examples or close variants are present in training data, inflating reported performance.

A collection of structured or unstructured examples used for training, validation, or testing.

Very large and complex datasets that require scalable storage and processing techniques.

Training approaches that let a model keep learning from new data without forgetting prior knowledge.

Continuing training on domain-specific data to adapt a pre-trained model to a specific task.