What are common pitfalls in implementing Generative AI pipelines for data synthesis

Question

With the help of proper code explanation can you tell me What are common pitfalls in implementing Generative AI pipelines for data synthesis?

Answer 1

Common pitfalls in implementing Generative AI pipelines for data synthesis include:

• Insufficient Data Quality: Training on low-quality or biased data leads to poor or unrepresentative synthetic outputs.
• Overfitting: The model memorizes training data instead of learning generalizable patterns.
• Mode Collapse: The generator produces limited variations, reducing diversity in synthesized data.
• Lack of Evaluation Metrics: Failing to use robust metrics like FID or precision-recall for quality assessment.
• Privacy Risks: Synthesized data inadvertently reveals sensitive information from the training set.

Here is the code snippet you can refer to:

In the above code we are using the following:

• Diversity Regularization: Adds constraints to mitigate mode collapse and improve output variability.
• Balanced Training: Ensures generator and discriminator stay competitive during training.
• Evaluation Metrics: Use metrics like FID to monitor quality.

Hence, by addressing these pitfalls, you can ensure robust, high-quality synthetic data generation with Generative AI pipelines.