Padding deep learning

1. What is padding in deep learning?
2. What is padding used for in CNN?
3. What does padding =' Same mean?
4. What is the role of padding?

What is padding in deep learning?

Padding is simply a process of adding layers of zeros to our input images so as to avoid the problems mentioned above. This prevents shrinking as, if p = number of layers of zeros added to the border of the image, then our (n x n) image becomes (n + 2p) x (n + 2p) image after padding.

What is padding used for in CNN?

In order to assist the kernel with processing the image, padding is added to the frame of the image to allow for more space for the kernel to cover the image. Adding padding to an image processed by a CNN allows for more accurate analysis of images.

What does padding =' Same mean?

The padding type is called SAME because the output size is the same as the input size(when stride=1). Using 'SAME' ensures that the filter is applied to all the elements of the input. Normally, padding is set to "SAME" while training the model. Output size is mathematically convenient for further computation.

What is the role of padding?

Padding is used to create space around an element's content, inside of any defined borders.