To handle latent space collapse in a VAE when training on real-time data, you can follow the following steps:
- Introduce Latent Space Regularization: Apply regularization methods like beta-VAE to encourage disentangled latent variables and prevent collapse.
- Increase Latent Space Dimension: Use a larger latent space to provide more capacity for the model to learn diverse representations.
- Use Scheduled Sampling: Gradually introduce noise in the latent space during training to prevent overfitting and encourage exploration.
- Early Stopping: Implement early stopping based on validation performance to avoid the model getting stuck in a collapsed state.
Here is the code snippet you can refer to:
In the above code, we are using the following:
- Beta-VAE: Increasing the beta parameter increases the regularization on the KL divergence term, encouraging the model to learn more disentangled and diverse latent representations.
- Larger Latent Space: A higher-dimensional latent space provides the model with more capacity to represent diverse features of the data.
- Regularization: Encouraging the model to learn a more structured latent space can help avoid collapse, especially with real-time data.
Hence, these techniques help mitigate latent space collapse when training VAEs on real-time data.
