  In this note, we want to focus on aspects related to two questions most
people asked us at CVPR about the network we presented. Firstly, What is the
relationship between our proposed layer and the deconvolution layer? And
secondly, why are convolutions in low-resolution (LR) space a better choice?
These are key questions we tried to answer in the paper, but we were not able
to go into as much depth and clarity as we would have liked in the space
allowance. To better answer these questions in this note, we first discuss the
relationships between the deconvolution layer in the forms of the transposed
convolution layer, the sub-pixel convolutional layer and our efficient
sub-pixel convolutional layer. We will refer to our efficient sub-pixel
convolutional layer as a convolutional layer in LR space to distinguish it from
the common sub-pixel convolutional layer. We will then show that for a fixed
computational budget and complexity, a network with convolutions exclusively in
LR space has more representation power at the same speed than a network that
first upsamples the input in high resolution space.
