Convolutional neural networks frequently use kernels with odd spatial dimensions, such as $1 imes 1$, $3 imes 3$, $5 imes 5$, or $7 imes 7$. Choosing odd kernel sizes provides the advantage of preserving dimensionality while allowing for symmetric padding—adding the exact same number of rows on the top and bottom ($p_ extrm{h}/2$ rows on each side), and the same number of columns on the left and right. This symmetric padding also ensures a clerical benefit: for any input tensor, the output element at index $(i, j)$ is computed by a cross-correlation window centered exactly on the input element at $(i, j)$.