To solve generator divergence when applying VAEs for complex image generation tasks, you can follow the following:
- Adjust the KL Divergence Weight: If the KL divergence term is too large, it can cause the generator to collapse. Gradually increase the weight of the KL divergence term (KL warm-up).
- Use a More Powerful Decoder: Using a deeper or more complex decoder network can help the generator produce better images and avoid divergence.
- Add a Regularization Term: Introduce a regularization term, such as the total variation loss, to reduce noise and improve image quality.
- Use a Better Optimizer: Switch to optimizers like Adam with adaptive learning rates to stabilize training.
Here is the code snippet you can refer to:
In the above code, we are using the following:
- KL Divergence Warm-up: Gradually increase the KL divergence weight to avoid its overpowering effect on the generator during the early training phase.
- Learning Rate Adjustment: Use an adaptive optimizer (like Adam) to adjust the learning rate and prevent divergence.
- Regularization: Add regularization techniques like total variation loss to improve the stability of the model.
Hence, these techniques help mitigate generator divergence during the training of VAEs for complex image generation tasks.
