PixelShuffle is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of $1/r$. Specifically it rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$.
Image Source: Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement
Source: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural NetworkPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Super-Resolution | 39 | 33.33% |
Image Super-Resolution | 20 | 17.09% |
Video Super-Resolution | 9 | 7.69% |
Decoder | 2 | 1.71% |
Semantic Segmentation | 2 | 1.71% |
Denoising | 2 | 1.71% |
Optical Flow Estimation | 2 | 1.71% |
Quantization | 2 | 1.71% |
Video Restoration | 2 | 1.71% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |