How would you fix training instability when working with a VAE in a semi-supervised learning task

Question

Can you tell me how you would fix training instability when working with a VAE on a semi-supervised learning task?

Answer 1

To fix training instability when working with a VAE in a semi-supervised learning task, you can follow the following key points:

• Use a Weighted Loss: Incorporate a weighted loss for the labeled data to ensure it dominates the loss function.
• Regularization: Add a KL divergence regularization term to ensure smooth latent space learning.
• Adjust Learning Rate: Decrease learning rate or use adaptive learning rate methods (e.g., Adam optimizer).
• Batch Normalization: Use batch normalization in the encoder/decoder to stabilize training.
• Semi-Supervised Loss: Combine unsupervised reconstruction loss with supervised classification loss for labeled data.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Weighted Loss: Balances reconstruction and supervised classification tasks.
• KL Divergence: Regularizes the latent space to prevent overfitting.
• Adaptive Optimizer: This tool uses Adam to manage learning rates dynamically.

Hence, these steps help stabilize training in semi-supervised VAE tasks by ensuring a balance between labeled and unlabeled data.