Augmentation Techniques

Augmentation techniques are methods used to improve machine learning models by increasing the diversity and amount of training data. For example, in image processing, this might involve rotating, flipping, or adjusting the brightness of images to create new, varied examples. This helps the model learn to recognize objects in different conditions and reduces overfitting. In natural language processing, augmentation can include paraphrasing sentences or inserting synonyms. Overall, these techniques enhance a model’s ability to generalize better to new, unseen data without needing to collect more original data.