What are the limitations of batch normalization?

What are the limitations of batch normalization?

Batch normalization can be limited by its dependency on batch size, which can affect performance in small batches.

Can it cause issues with certain types of data?

Yes, batch normalization may not perform well with non-iid data or when the distribution of data changes.

Are there alternatives to batch normalization?

Yes, alternatives include layer normalization, instance normalization, and group normalization, each with its own use cases.

How does it affect training speed?

Batch normalization can speed up training by allowing higher learning rates, but it may introduce overhead during inference.

Is it always beneficial to use batch normalization?

Not necessarily; its benefits depend on the specific architecture and dataset, and it may even hinder performance in some cases.