How do you fix latent code bias in a VAE model used for generating diverse scene variations

Question

Can you tell me How do you fix latent code bias in a VAE model used for generating diverse scene variations?

Answer 1

Mitigate latent code bias in a Variational Autoencoder (VAE) by incorporating disentangled representations, reweighted sampling, fairness constraints, and adversarial debiasing.

Here is the code snippet you can refer to:

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

• β-VAE for Disentanglement:
  • Uses a higher β (e.g., 4.0) to enforce independent latent dimensions, reducing bias.
• Reweighted Sampling Strategy:
  • Encourages fair representation of underrepresented features in the latent space.
• Fairness-Optimized KL Divergence:
  • Adjusts KL term to prevent specific features from dominating latent encoding.
• Adversarial Debiasing (Optional Enhancement):
  • Can introduce an adversarial loss to discourage biased latent distributions.

Hence, by employing β-VAE for disentangled learning, reweighted sampling, and fairness constraints, the model prevents latent code bias and ensures diverse, unbiased scene variations in VAE-generated outputs.