Basically making up new data from existing one for better, more comprehensive training.
In a single image, you might augmentate it with simple transformations
- transaltions
- resizings
- recolorings
- shading
- increasing borders
- changing transparency
Data augmentation enhances machine learning models by creating varied data versions, boosting accuracy, and reducing data collection costs. Common in image recognition and NLP, it leverages techniques like GANs, word swaps, and neural style transfer