# Transposed Conv - use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution. - Upsampling - Input i, kernel k, padding p, stride s , $o = (i-1) \times s +k -2p$ - Steps - Calculate new Param's z and p' - Between each row and columns of the input, insert z number of zeros. This increases the size of the input to $(2*i -1) \times (2*i -1)$ - Pad the modified image with p' no of zeros - Standard conv with stride of 1 -