dataforstørrelse

Dataforstørrelse, often translated as data augmentation, is a technique used in machine learning to artificially increase the size of a training dataset by creating modified versions of existing data. This process is particularly useful when the original dataset is small, as it helps to improve the robustness and generalization ability of machine learning models. By exposing the model to a wider variety of data, it becomes less likely to overfit to the specific examples in the original training set.

The specific methods of data augmentation vary depending on the type of data. For image data, common techniques include geometric transformations such as rotations, translations, scaling, and cropping. Color space transformations, like adjusting brightness, contrast, or saturation, are also frequently employed. For text data, augmentation might involve word or phrase replacement with synonyms, random insertion or deletion of words, or back-translation. In the case of audio data, techniques can include adding background noise, changing the pitch or speed, or time-stretching.

The core idea behind data augmentation is to generate new training samples that are plausible variations of the original data, without altering the underlying meaning or label of the data. This allows models to learn more invariant features, meaning they can recognize objects or patterns even when presented with slightly different appearances or contexts. Ultimately, effective data augmentation can lead to improved model performance, especially in scenarios where acquiring large amounts of labeled data is challenging or expensive.