What s the code for implementing latent variable inference in a VAE model for generating high-quality synthetic images

Question

Can you tell me the code for implementing latent variable inference in a VAE model to generate high-quality synthetic images?

Answer 1

To implement latent variable inference in a Variational Autoencoder (VAE) for generating high-quality synthetic images, you need to define the encoder to infer latent variables, the decoder to create images, and optimize the ELBO (Evidence Lower Bound) during training. Here is the code you can refer to:

In the above code, we are using the following:

• Latent Variable Inference: The encoder outputs μ\muμ (mean) and log⁡σ2\log\sigma^2logσ2 (log variance) to parameterize the latent variable distribution.
• Reparameterization Trick: Ensures differentiability for latent variable sampling.
• High-Quality Images: Use a good dataset, large latent dimensions, and regularization to improve quality.
• Decoder: Ensures synthesized images are realistic and aligned with the latent space.

Hence by referring to above you can implement latent variable inference in a VAE model for generating high-quality synthetic images