To fix latent space distortion in a VAE (Variational Autoencoder) used for genetic data generation, you can follow the following steps:
- Use a More Structured Latent Space: Apply regularization techniques like Hierarchical VAEs or β-VAE to encourage better structure in the latent space.
- Increase the Latent Dimension: If the latent space is too small, increase the dimensionality to allow for more diverse representations of the genetic data.
- Use a More Complex Encoder/Decoder: Use deeper or more complex neural networks to capture the intricate relationships in genetic data.
- Use a Gaussian Prior: Ensure that the prior distribution of the latent space follows a standard Gaussian distribution, which is often more suitable for continuous data.
- Apply Reconstruction Loss Regularization: Add a stronger reconstruction loss to preserve original data better while learning the latent space.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- β-VAE: Controls the trade-off between reconstruction and latent space regularization, improving latent space structure.
- Latent Dimension Adjustment: Increasing the latent space size can capture more complexity in genetic data.
- Regularization: Enforcing a stronger reconstruction loss or using more complex models can improve latent space consistency.
Hence, these methods will help fix latent space distortion by making the latent space more structured and aligned with the distribution of genetic data.
