Data Augmentation
Techniques that artificially expand training datasets by creating modified versions of existing data to improve model performance.
Definition
Data augmentation generates additional training examples by applying transformations, modifications, or combinations to existing data. This approach helps models generalize better by exposing them to more diverse examples during training.
Augmentation techniques vary by data type, including image rotations, text paraphrasing, and audio pitch changes, while preserving essential characteristics that maintain label accuracy and training validity.
Why It Matters
Data augmentation improves model robustness and performance without requiring expensive additional data collection. This is particularly valuable when training data is limited or expensive to obtain.
Businesses can build more effective AI systems with existing datasets while reducing data collection costs and time-to-deployment for new AI applications and capabilities.
Examples in Practice
Computer vision systems augment training images with rotations, brightness changes, and crops to improve object recognition across different lighting and viewing conditions.
Natural language processing models use paraphrasing and synonym replacement to augment text datasets, improving understanding of language variations and expressions.
Speech recognition systems augment audio data with background noise, speed changes, and accent variations to improve accuracy across diverse speaking conditions.