A same convolution is a type of convolution where the output matrix is of the same dimension as the input matrix.

For a \(n \times n\) input matrix \(A\) and a \(f \times f\) filter matrix \(F\), the output of the convolution \(A * F\) is of dimension \(\left \lfloor \frac{n + 2p - f}{s} \right \rfloor + 1 \times \left \lfloor \frac{n + 2p - f}{s} \right \rfloor + 1\) where \(s\) represents the stride length and \(p\) represents the padding.

In a same convolution:

• \(s\) is typically set to \(1\)
• \(p\) is set to \(\frac{f - 1}{2}\)
• \(f\) is an odd number

The result is that \(A\) is padded to be \(n + p \times n + p\) and \(A * F\) becomes \(n \times n\) – the same as the original dimensions of \(A\).