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All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Shift operation is an efficient alternative over depthwise separable convolution. However, it is still bottlenecked by its implementation manner, namely memory movement. To put this direction forward, a new and novel basic component named Sparse Shift Layer (SSL) is introduced in this paper to construct efficient convolutional neural networks. In this family of architectures, the basic block is only composed by 1x1 convolutional layers with only a few shift operations applied to the
doi:10.1109/cvpr.2019.00741
dblp:conf/cvpr/ChenXZP19
fatcat:fwvpjqo63za7doqan2hdmyy5qa