- Ensemble methods
Combine models trained on different mixtures of synthetic and real data
Improves overall robustness and generalization
- Continual learning
Periodically update model with new real-world data
Maintains relevance to changing real-world conditions
- Extensive validation
Use diverse real-world test sets for evaluation
Identify and address specific generalization gaps