Data Augmentation Strategies

Create a data augmentation system to improve model generalization.

Data type: [IMAGE/TEXT/TABULAR/AUDIO]
Current dataset size: [SIZE]
Target improvement: [ACCURACY/ROBUSTNESS/GENERALIZATION]

Augmentation requirements:
1. Image augmentation:
   - Geometric (rotate, flip, crop, scale)
   - Color (brightness, contrast, saturation)
   - Advanced (mixup, cutout, mosaic)
   - Albumentations pipeline
2. Text augmentation:
   - Synonym replacement
   - Back-translation
   - Random insertion/deletion
   - Contextual word embeddings
3. Tabular augmentation:
   - SMOTE variants
   - Feature noise injection
   - Mixup for tabular
4. Audio augmentation:
   - Time stretching
   - Pitch shifting
   - Noise injection
5. Implementation:
   - On-the-fly vs. offline
   - Probability scheduling
   - Curriculum augmentation
6. Validation:
   - Augmentation impact analysis
   - Visualization tools
   - A/B testing

Balance augmentation strength with data fidelity.