What is Data Augmentation?
TL;DR
A technique for artificially increasing training data through transformations. Improves model accuracy via image rotations, text paraphrasing, and more.
Data Augmentation: Definition & Explanation
Data augmentation is a technique that artificially increases the variety of training data by applying transformations to existing samples. In computer vision, common transformations include rotation, flipping, scaling, color adjustment, and cropping. For text, synonym replacement, paraphrasing, and back-translation (translating to another language and back) are used. It improves model generalization and reduces overfitting, even with limited training data. Recently, generating synthetic data with generative AI has gained attention as a broader form of data augmentation. Tools like Stable Diffusion and DALL-E 3 are being used for synthetic image data generation in practical applications.