Transposed Convolutions explained with… MS Excel! | by Thom Lane | Apache MXNet | Medium

You’ve successfully navigated your way around 1D Convolutions, 2D Convolutions and 3D Convolutions. You’ve conquered multi-input and multi-output channels too. But for the last blog post in the convolution series we’re onto the boss level: understanding the transposed convolution. So let’s start with the name and see what we’re dealing with. A transpose “causes (two or more things) to change places with each other”. When we’re transposing matrices we change the order of their dimensions, so for a 2D matrix we essentially ‘flip’ values with respect to the diagonal. We won’t be covering this in the series, but it’s possible to represent operations (such as rotations, translations, and convolutions) as matrices. See Section 4.1 of Dumoulin & Visin if you’re interested. When we’re transposing convolutions we change the order of the dimensions in this convolution operation matrix, which has some interesting effects and leads to different behaviours to the regular convolutions we’ve learnt a

You’ve successfully navigated your way around 1D Convolutions, 2D Convolutions and 3D Convolutions. You’ve conquered multi-input and multi-output channels too. But for the last blog post in the convolution series we’re onto the boss level: understanding the transposed convolution. So let’s start with the name and see what we’re dealing with. A transpose “causes (two or more things) to change places with each other”. When we’re transposing matrices we change the order of their dimensions, so for a 2D matrix we essentially ‘flip’ values with respect to the diagonal. We won’t be covering this in