How can you address latent space inconsistency when training a VAE on heterogeneous data across multiple domains

Question

Can you tell me How can you address latent space inconsistency when training a VAE on heterogeneous data across multiple domains?

Answer 1

To address latent space inconsistency when training a Variational Autoencoder (VAE) on heterogeneous data across multiple domains, you can use the following strategies:

• Domain-Specific Encoders and Shared Latent Space
• Domain-Adversarial Training
• Cycle Consistency Loss
• Domain-Specific Loss Weighting
• Training Step

Here are the code snippets you can follow:

In the above code, we are using the following strategies:

• Domain-Specific Encoders: Separate encoders for each domain improve latent space mapping.
• Shared Decoder: Ensures consistent latent-to-data mapping across domains.
• Adversarial Training: Enforces domain invariance in the latent space.
• Cycle Consistency Loss: Maintains consistency between domains and latent representations.
• Weighted Loss: Helps balance heterogeneous data contributions during training.

Hence, these techniques collectively address latent space inconsistency and improve training robustness on heterogeneous datasets.