Which CNN model parameter helps maintain the same input and output size in the convolutional layer?

In convolutional neural networks (CNNs), maintaining the same input and output size in a convolutional layer is achieved through the use of padding. Padding adds extra pixels to the input image or feature map, which compensates for the reduction in spatial dimensions that occurs due to the convolution operation.

When a convolutional layer applies a filter or kernel to the input, it typically reduces the dimensions of the output. For example, using a 3x3 kernel on a 5x5 input will reduce the dimensions to 3x3, as the filter only fully overlaps a portion of the input, leaving out boundaries. By adding padding around the input — typically referred to as "same" padding — the input size can be preserved. This means that the output size will remain equal to the input size, allowing for easier stacking of layers and maintaining spatial hierarchies.

The kernel size determines the dimensions of the filter being applied, while the stride controls how much the filter moves across the input. Both of these parameters can influence the output size, but it is padding that specifically ensures that the output dimensions remain consistent with the input dimensions when desired. The activation function, on the other hand, applies a non-linear transformation after the convolution operation and does not