  Deep convolutional neural networks achieve remarkable visual recognition
performance, at the cost of high computational complexity. In this paper, we
have a new design of efficient convolutional layers based on three schemes. The
3D convolution operation in a convolutional layer can be considered as
performing spatial convolution in each channel and linear projection across
channels simultaneously. By unravelling them and arranging the spatial
convolution sequentially, the proposed layer is composed of a single
intra-channel convolution, of which the computation is negligible, and a linear
channel projection. A topological subdivisioning is adopted to reduce the
connection between the input channels and output channels. Additionally, we
also introduce a spatial "bottleneck" structure that utilizes a
convolution-projection-deconvolution pipeline to take advantage of the
correlation between adjacent pixels in the input. Our experiments demonstrate
that the proposed layers remarkably outperform the standard convolutional
layers with regard to accuracy/complexity ratio. Our models achieve similar
accuracy to VGG, ResNet-50, ResNet-101 while requiring 42, 4.5, 6.5 times less
computation respectively.
