Padding

Padding, in the context of convolutional neural networks, refers to adding extra layers of "pixels" or values around the input image or feature map. This is typically done with zeros (zero-padding), but other values can be used. The primary purpose of padding is to control the spatial size of the output feature maps and to manage boundary effects that arise during convolution operations. By strategically adding padding, we can preserve the original input size, prevent information loss at the edges, and improve the performance of the network.

Visit the following resources to learn more:

• @article@Padding In Convolutional Neural Networks
• @video@Convolution padding and stride