AI Glossary/Data Augmentation
Data Science

Data Augmentation

Artificially increasing training data by creating modified versions of existing data.

In-depth explanation

Data augmentation applies transformations to training data to create new examples, improving model robustness and reducing overfitting. For images: rotation, flipping, cropping, color adjustment. For text: synonym replacement, back-translation, paraphrasing. For audio: speed change, pitch shift, noise addition. Augmentation is especially valuable with limited data.

Examples

Image rotation and flipping
Text paraphrasing
Audio noise addition

Related terms

Overfitting

More in Data Science

Data Preprocessing

Cleaning and transforming raw data into a format suitable for machine learning.

Train-Test Split

Dividing data into separate sets for training and evaluating model performance.

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