Data Augmentation
Techniques for artificially increasing training data by creating modified versions of existing data.
In-Depth Explanation
Data augmentation creates new training samples by applying transformations to existing data. It improves model generalisation and helps when training data is limited.
Image augmentation:
- Rotation, flipping, cropping
- Scaling, translation
- Color jitter, brightness
- Noise addition
- Elastic deformation
Text augmentation:
- Synonym replacement
- Back-translation
- Random insertion/deletion
- Sentence shuffling
- Paraphrasing (LLM-based)
Audio augmentation:
- Time stretching
- Pitch shifting
- Noise addition
- Speed changes
Benefits:
- Reduce overfitting
- Improve generalisation
- Handle data imbalance
- Increase effective dataset size
Business Context
Data augmentation is a cost-effective way to improve model performance when collecting more real data is expensive or impractical.
How Clever Ops Uses This
We apply appropriate data augmentation for Australian business AI projects, improving model robustness without additional data collection.
Example Use Case
"Training an image classifier with augmented images: each original image generates 10 variants with different rotations, crops, and lighting conditions."