Paper Info
Title
Is the deconvolution layer the same as a convolutional layer?
Abstract
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.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
CLARITY,Convolution,Computer science,Algorithm,Deconvolution,Theoretical computer science,Artificial intelligence,Machine learning
DocType
Volume
Citations 
Journal
abs/1609.07009
9
PageRank 
References 
Authors
0.72
4
7
Name
Order
Citations
PageRank
Wenzhe Shi179239.85
Jose Caballero266322.59
Theis, Lucas336825.90
Ferenc Huszar458322.66
andrew aitken53378.57
Christian Ledig648927.08
Zehan Wang736911.51