Data Augmentation

Data augmentation is a technique used in machine learning and artificial intelligence to artificially expand the size and diversity of a training dataset. It involves creating variations of existing data points by applying transformations such as rotation, scaling, flipping, cropping, or adding noise to images; altering the text in natural language processing; or generating synthetic samples in various ways. The primary purpose of data augmentation is to improve the robustness and generalizability of models, helping them to perform better on unseen data by exposing them to a wider range of scenarios that they might encounter in real-world applications. This approach helps to mitigate issues such as overfitting, where a model learns to perform well on the training data but fails to generalize to new, unseen data. Data augmentation is particularly popular in fields like computer vision and speech recognition, where it can significantly enhance model performance without the need for collecting large amounts of additional labeled data.