Description

Data augmentation refers to techniques used to increase the amount and diversity of training data without actually collecting new data. This is typically done by applying various transformations to existing data points. Data augmentation helps improve model generalization, reduces overfitting, and can make models more robust to variations in input data. It's particularly useful when working with limited datasets or when collecting additional data is expensive or impractical.

Examples

• 🔄 Image rotation, flipping, or cropping
• 🔊 Adding noise to audio samples
• 🔄 Synonym replacement in text

Applications