Convnets : do we have separate activation maps for images in a batch

Question

I know that if we input an image of shape [6,128] to a convolutional layer with 5 filters each of shape[3,128] with S=1 and P=0 , then there will be 5 activation maps as output from the layer ..each map has a shape of [4,1]

But what about the number of maps outputted when we use batching ?

I mean if we are using a batch with size of 2 images (each of shape[6,128]) as input to a convolutional layer with 5 filters each of shape[3,128] with S=1 and P=0 also , then there will be 10 activation maps as output from the layer instead of 5 (each image in the batch has 5 maps) ?? or we will have also 5 maps but each of shape[2,4,1] ???

I think there will be 10 maps since if we apply then max pooling , we want to choose the max from each image independently , right? so each image must have separate maps from the maps of the other image..

Answer 1

Yes, separate maps. The number of the image in the batch is another dimension.

Max pool is performed on a per-image basis, other layers operate on an entire batch - e.g. batch normalisation.