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 operations such as rotation, scaling, cropping, flipping, shearing, and color jittering. For text data, techniques like synonym replacement, random insertion, random deletion, and sentence shuffling can be employed. In the case of audio data, augmentation might include adding noise, changing pitch, or altering the speed.

The application of data augmentation is particularly beneficial when the original dataset is small or lacks sufficient variety. By exposing the model to a wider range of variations, it learns to be less sensitive to minor changes in the input data. This can lead to more accurate predictions and a more reliable model in real-world scenarios where data is rarely perfectly consistent. Data augmentation is a cost-effective method compared to manually collecting and labeling more data.