Adataumentáció
Adataumentáció is a technique used in machine learning and artificial intelligence to artificially increase the size and diversity of a training dataset. This is achieved by creating new data points from existing ones through various transformations. The primary goal is to improve the robustness and generalization capabilities of machine learning models, preventing overfitting and enhancing their performance on unseen data.
Common data augmentation techniques vary depending on the type of data. For images, augmentation might involve
The application of data augmentation is particularly beneficial when the original dataset is small or lacks