I've created a comprehensive list of strategies to improve real-world generalization for models trained on synthetic data. These approaches aim to bridge the gap between synthetic and real-world data, ensuring that models can perform well in practical applications.
Key points to emphasize:
Mixing real and synthetic data is crucial. Even a small amount of real-world data can significantly improve generalization.
Diverse training approaches like domain randomization and curriculum learning help models learn more robust features.
Continuous evaluation and updating are essential. The real world changes, and models need to adapt.
Quality of synthetic data matters immensely. Investing in high-quality synthetic data generation can greatly reduce generalization issues.