What is the difference between batch normalization and layer normalization?

What is the difference between batch normalization and layer normalization?

Batch normalization normalizes the inputs of each layer across the batch, while layer normalization normalizes across the features for each individual sample.

When should I use batch normalization over layer normalization?

Use batch normalization when training deep networks with large batch sizes, as it helps stabilize learning. Layer normalization is better for recurrent networks or when batch sizes are small.

Can you explain how each method affects training speed?

Batch normalization can speed up training by allowing higher learning rates and reducing internal covariate shift. Layer normalization, while generally slower, can provide more stable training in certain architectures.

Are there any downsides to using these normalization techniques?

Yes, batch normalization can introduce dependency on the batch size, while layer normalization may not perform as well in convolutional networks.