Adataumentációval

Adataumentációval refers to the process of artificially increasing the size of a training dataset by creating modified copies of existing data. This technique is commonly employed in machine learning, particularly in deep learning, to improve the robustness and generalization performance of models. By exposing the model to a wider variety of augmented examples, it becomes less sensitive to minor variations in the input data and better equipped to handle unseen data.

The specific augmentation techniques used depend heavily on the type of data. For image data, common methods include geometric transformations such as rotation, scaling, cropping, and flipping. Color transformations like adjusting brightness, contrast, and saturation are also frequently applied. For text data, techniques like synonym replacement, random insertion or deletion of words, and back-translation can be used. Audio data might undergo pitch shifting, time stretching, or adding background noise.

The primary goal of adataumentációval is to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and specific details, leading to poor performance on new, unseen data. By generating more diverse training examples, the model is encouraged to learn more generalizable features. This augmentation is typically performed on-the-fly during the training process, meaning the augmented data is generated and fed to the model in batches, rather than creating a massive, pre-augmented dataset. This saves storage space and allows for a potentially infinite number of variations.