Can Generative AI models be used for high-fidelity synthetic data generation?

All answers to this question.

Synthetic data is a game-changer for niche domains. GANs were the standard, but Diffusion Models are now producing higher-fidelity results with better stability. The biggest risk is indeed "Mode Collapse," where the generator starts producing the same few "safe" images over and over. To prevent this, use techniques like "Mini-batch discrimination" or move to VAEs (Variational Autoencoders). Also, ensure you use "Differential Privacy" during training so the synthetic data doesn't accidentally leak real patient features, which could violate HIPAA or GDPR regulations.

   Answered 2024-07-07 by Barbara Taylor

How are you planning to validate the synthetic data? If you just train a model on AI-generated data, you might end up with "model collapse" where the new model learns the errors of the generator rather than real-world physics.

   Answered 2024-07-08 by Richard Clark

• Richard, we are actually using a "TSTR" (Train on Synthetic, Test on Real) approach. We train our diagnostic model on the synthetic images but only validate it against a small, "gold-standard" set of real clinical scans. This tells us exactly if the synthetic data is helping or hurting our accuracy. So far, we’ve seen a 12% improvement in edge-case detection because the generator can create rare pathology examples that we simply don't see enough in our limited real-world dataset.

     Commented 2024-07-10 by Mark Peterson

Diffusion models are definitely more stable than GANs for this. They don't suffer from the same adversarial training instability that leads to mode collapse so easily.

   Answered 2024-07-11 by Jennifer Davis

• I agree with Jennifer. Since we switched to Stable Diffusion for our medical imaging augmentation, the variety in our datasets has improved significantly compared to our old GAN setups.

     Commented 2024-07-12 by Barbara Taylor