1x1 convolutions are used to reduce the dimensionality of filter space. I referred this answer.
But we can also reduce the dimensionality of filter space (number of filters) using convolutions other than
1x1 using padding "SAME".
So, What is the difference between those two approaches? One difference I could figure out from the above mentioned question is that applying higher convolutions are computationally expensive and hence first
1x1 convolutions are used to reduce the number of filters and then higher size convolutions are applied. Reference: This answer.
Is there any other significant difference between those two?