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.

本论文中，我们重点关注了关于我们所提出的网络模型的两个问题：我们提出的层和反卷积层之间的关系，以及为什么低分辨率空间中的卷积是更好的选择。通过实验我们发现对于相同的速度，将所有卷积都放在低分辨率空间中，比先在高分辨率空间中插值再进行卷积的方法拥有更强的表示能力。

反卷积层和卷积层是否相同？