What does pixel shuffle do?

PixelShuffle is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of . Specifically it rearranges elements in a tensor of shape ( ∗ , C × r 2 , H , W ) to a tensor of shape ( ∗ , C , H × r , W × r ) .

What is Channel Shuffle?

Channel Shuffle is an operation to help information flow across feature channels in convolutional neural networks. It was used as part of the ShuffleNet architecture. If we allow a group convolution to obtain input data from different groups, the input and output channels will be fully related.

What is subpixel convolution?

Sub-pixel convolution [1,14] is a specific implementation of a deconvolution layer that can be interpreted as a standard convolution in low-resolution space followed by a periodic shuffling operation as shown in Figure 2.

What is group convolution?

A Grouped Convolution uses a group of convolutions – multiple kernels per layer – resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features.

What is a transposed convolution?

Transposed convolutions are standard convolutions but with a modified input feature map. The stride and padding do not correspond to the number of zeros added around the image and the amount of shift in the kernel when sliding it across the input, as they would in a standard convolution operation.

What is Super Resolution deep learning?

Super Resolution is the process of recovering a High Resolution (HR) image from a given Low Resolution (LR) image. An image may have a “lower resolution” due to a smaller spatial resolution (i.e. size) or due to a result of degradation (such as blurring).

What is Depthwise separable convolution?

While standard convolution performs the channelwise and spatial-wise computation in one step, Depthwise Separable Convolution splits the computation into two steps: depthwise convolution applies a single convolutional filter per each input channel and pointwise convolution is used to create a linear combination of the …

What is ResNeXt?

ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology.

What is sub Pixeling?

Basically, “sub-pixel animation” means animating your anti-aliasing. A valuable technique either for transitioning shades of colors inside a sprite from light to shadow or in slightly moving an outline without moving the silhouette.

What is groups in CNN?

Groups controls the connections between inputs and outputs. in_channels and out_channels must both be divisible by groups. For example, At groups=1, all inputs are convolved to all outputs.

Why transposed convolution is used?

Transposed Convolutions are used to upsample the input feature map to a desired output feature map using some learnable parameters.

What is Dilation_rate?

Dilated Convolutions are a type of convolution that “inflate” the kernel by inserting holes between the kernel elements. An additional parameter (dilation rate) indicates how much the kernel is widened. There are usually spaces inserted between kernel elements.