dataaugmantatie

Data augmentation is a technique used in machine learning and deep learning to artificially increase the size and diversity of a training dataset. This is achieved by applying various transformations to existing data samples, creating new, modified versions that are still representative of the original data's characteristics. The primary goal of data augmentation is to improve the robustness and generalization ability of machine learning models, preventing them from overfitting to the specific training examples and performing better on unseen data.

Common data augmentation techniques vary depending on the type of data. For images, transformations include rotations, scaling, cropping, flipping, shearing, and adjusting brightness or contrast. For text data, techniques might involve synonym replacement, random insertion or deletion of words, or back-translation. In audio processing, augmentation can involve changing pitch, speed, or adding background noise.

By generating more varied training examples, data augmentation helps models learn more invariant features. For instance, an image classifier trained with augmented images that have been rotated will be less sensitive to the orientation of objects in new, unrotated images. This is particularly useful when the available dataset is small or lacks sufficient diversity, which is a common challenge in many real-world machine learning applications. The process is typically applied during the training phase, where augmented data is generated on-the-fly or pre-generated and added to the original dataset.