Degradation concerns:
The risk of degradation across generations (often called "model collapse" or "mode collapse" in some contexts) is a valid concern. However, it's not inevitable if proper techniques are employed:
- Mixing real and synthetic data: Using a combination can help maintain fidelity to real-world patterns.
- Periodic recalibration: Regularly incorporating new real data into the generative process.
- Quality metrics: Implementing robust evaluation methods to ensure synthetic data quality.
- Advanced techniques: Using methods like differential privacy or cycle-consistent adversarial networks to preserve data characteristics.