You can implement batch normalization for stability when training GANs or VAES by referring to the following techniques.
- Placement of Batch Normalization Layers: Batch normalization is applied after the fully connected layers and before the activation functions. This order ensures that the activations are normalized before being passed through the non-linear transformations, thus stabilizing the learning process.
- Choosing the Right Mini-Batch Size: The size of the mini-batch used during training can influence the effectiveness of batch normalization. Smaller batch sizes can lead to noisier estimates of the batch statistics, while larger batch sizes provide more stable estimates but require more computational resources.
- Handling Small Batch Sizes: Strategies exist to handle the limitations of small batch sizes in scenarios where using large batch sizes is not feasible, such as when working with very high-resolution images or limited computational resources.
- Monitoring Training Metrics: You can Implement batch normalization, which requires careful monitoring of training metrics to ensure that it has the desired effect. Key metrics to monitor include the training and validation loss, learning rate, and the stability of the gradients.
- Fine-Tuning Batch Normalization Parameters: Batch normalization involves parameters such as momentum and epsilon that can be fine-tuned to optimize performance. Momentum determines how much of the past batch statistics to retain, while epsilon is a small constant added to the variance to prevent division by zero.
By using these five techniques, you can implement batch normalization for stability when training GANs and VAEs.
