Batch normalization vs batch size I have noticed that my performance of VGG 16 network gets better if I increase the batch size from $64$ to $256$. I have also observed that, using batch size $64$, the with and without batch normalization results have lot of difference. With batch norm results being poorer. As I increase the batch size the performance of with and without batch normalization gets closer. Something funky going on here. 

So I would like to ask is the increase in batch size have effect on batch normalization?
By increasing batch size your steps can be more accurate because your sampling will be closer to the real population. If you increase the size of batch, your batch normalisation can have better results. The reason is exactly like the input layer. The samples will be closer to the population for inner activations.