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I recently read Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network by Wenzhe Shi et al. I cannot understand the difference between deconvolution (mentioned in Section 2.1) and the Efficient sub-pixel convolution layer (ESCL for short) (Section 2.2)

Section 2.2 defines the ESCL as performing convolution with a fractional stride. Isn't this what deconvolution is? Why is a distinction being made?

Thanks

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  • $\begingroup$ Theory is good. But in practice I getting much clear result with sub-pixel conv rather than deconv, also sub-pixel learns faster. $\endgroup$ – iperov Dec 31 '18 at 8:09
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In this paper, theauthors have provided a deep explanation, basically its the exact same thing. sub-pixel uses or^2 conv kernels all at res H/r,W/r while the regular transpose conv uses o conv kernels all at res H,W the authors prove that one can rearrange the feature map (Or^2,h,w) to a feature map (O,hr,wr) without losing information.

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