Data Forest logo
Home page  /  Glossary / 
Data Augmentation

Data Augmentation

Data Augmentation involves techniques to increase the diversity of training data by applying transformations to existing data samples. Methods such as rotation, scaling, and cropping for images, or paraphrasing and synonym replacement for text, create variations of the original data without collecting new samples. Data augmentation helps improve the generalization and robustness of machine learning models by exposing them to a wider range of scenarios and variations, which can lead to better performance and reduced overfitting.

Generative AI
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Latest publications

All publications
Preview article image
October 4, 2024
18 min

Web Price Scraping: Play the Pricing Game Smarter

Article image preview
October 4, 2024
19 min

The Importance of Data Analytics in Today's Business World

Generative AI for Data Management: Get More Out of Your Data
October 2, 2024
20 min

Generative AI for Data Management: Get More Out of Your Data

All publications
top arrow icon