To handle stagnant training progress in Variational Autoencoders (VAEs) for image generation tasks, you can take the following steps:
- Adjust Learning Rate: Use a learning rate scheduler or adjust the learning rate manually to ensure the model is not stuck at a local minima.
- Improve Latent Space Regularization: Strengthen the KL divergence term to ensure better latent space exploration.
- Use a Better Architecture: Experiment with deeper or more complex architectures such as convolutional VAEs (CVAE) for improved feature extraction.
- Warm-up the KL Divergence: Gradually increase the weight on the KL divergence term during the initial training phase to prevent the model from ignoring it.
- Use Data Augmentation: Apply transformations like rotations, flips, or color jitter to augment the dataset, introducing more variability to help the model generalize better.
Here is the code snippet you can use:
In the above code, we are using the following key points:
- KL Divergence Warm-up: Gradually increase the weight on the KL divergence term to prevent the model from ignoring it in early training.
- Learning Rate Adjustment: Use an adaptive learning rate to help escape local minima.
- Latent Space Regularization: Ensure the latent space is well-regularized to avoid stagnation.
Hence, these techniques should help improve the convergence rate and prevent stagnant progress when training VAEs for image generation tasks.
