What is a potential use of data augmentation techniques?

Data augmentation techniques are primarily employed to enhance the variability and robustness of the training dataset. By applying various transformations to the existing data, such as rotation, scaling, flipping, or adding noise, data augmentation artificially expands the training dataset. This increased diversity allows models to better generalize to new, unseen data, ultimately improving performance in tasks such as image recognition, natural language processing, and more.

The main goal of data augmentation is to simulate variations that the model may encounter in real-world applications, enabling it to learn more robust features and reducing the likelihood of overfitting. This ensures that the model does not simply memorize the training examples but instead learns to identify the underlying patterns that are important for making accurate predictions.

While dimensionality reduction focuses on compressing the data to reduce complexity, data augmentation specifically targets the enhancement of the dataset's richness. Additionally, eliminating outliers is a separate data preprocessing step that does not relate to augmentation, and simplifying model architecture is a different aspect of model design that does not involve the dataset's variability.