How do you optimize latent vector sampling in VAEs for better synthetic data generation

Question

With the help of code can you tell me How do you optimize latent vector sampling in VAEs for better synthetic data generation?

Answer 1

Optimize latent vector sampling in VAEs using prior regularization, importance-weighted sampling, and normalizing flows for more expressive latent distributions.Here is the code snippet you can refer to:

In the above code, we are using the following key approaches

• Reparameterization Trick: Enables backpropagation through the latent sampling process.

• KL Divergence Regularization: Ensures a well-structured latent space for improved sampling.

• Adaptive Sampling Strategy: Enhances latent vector expressiveness for diverse synthetic data.

• Scalability: Extendable to normalizing flows or importance-weighted sampling for richer data.

Hence, optimizing latent vector sampling in VAEs with KL divergence and reparameterization improves the quality and diversity of synthetic data generation.