How do I prevent a VAE from generating overly simplistic outputs when training on complex data

Question

Can you tell me How do I prevent a VAE from generating overly simplistic outputs when training on complex data?

Answer 1

To prevent a Variational Autoencoder (VAE) from generating overly simplistic outputs, use a higher-capacity latent space, apply a stronger decoder, fine-tune the β-VAE loss (KL weight), and enhance training diversity with richer datasets.

Here is the code snippet you can refer to:

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

• Increased Latent Space (latent_dim=10)

  • Allows the model to encode more meaningful variations.

• Enhanced Decoder (intermediate_dim=128)

  • Strengthens generative capacity, preventing blurry or simplistic outputs.

• Adjusted KL Loss Weight (kl_weight=0.1)

  • Balances regularization and reconstruction, avoiding excessive constraint on the latent space.

• Training with Normalized Data (x_train / 255.0)

  • Ensures stable optimization and better reconstruction fidelity.

Hence, by increasing latent space, strengthening the decoder, adjusting KL weight, and using well-preprocessed data, VAE can generate richer and more complex outputs while maintaining meaningful structure.