Augmentation is a technique that involves creating additional training examples by altering input images. This approach helps prevent machine learning models from overfitting to particular training data. Common augmentation methods include flipping images horizontally or vertically, rotating them by specific angles, applying blurring effects, and introducing various types of noise. By diversifying the training dataset in this way, models can learn more generalizable features and perform better on unseen data.
