When applying standard cross-correlation operations without padding, we encounter two main limitations. First, the spatial dimensions of the output shrink after each layer, which restricts the total number of successive convolutional layers that can be applied before the feature map is reduced to a $1 imes 1$ size. Second, because the convolution kernel must fit entirely within the image boundaries, the pixels on the perimeter and corners participate in far fewer window calculations than the central pixels, leading to a significant loss of boundary information.