In a convolutional operation, what happens when the padding is set to 'same'?

When padding is set to 'same' in a convolutional operation, it ensures that the output size remains the same as the input size. This is achieved by adding zero-padding around the input matrix, allowing the convolutional filter to be applied in such a way that the spatial dimensions (height and width) of the output feature map match those of the input.

This approach is beneficial in preserving spatial information throughout the layers of a neural network, as it prevents the reduction of dimensions that can occur with other types of padding, such as 'valid', which does not include padding and typically leads to a smaller output size. By maintaining the output size, deeper layers in the model can extract features effectively without losing important spatial details, which is particularly crucial in tasks like image classification and object detection.

The design choice to use 'same' padding plays a significant role in deep learning models, especially when stacking multiple convolutional layers, as it simplifies the architecture by keeping consistent sizes across layers.