Transposed convolutional layer

1. What is transposed convolutional layer?
2. What is transposed convolution used for?
3. How do you calculate transposed convolution?
4. Is transposed convolution same as deconvolution?

What is transposed convolutional layer?

The transposed Convolutional Layer is also (wrongfully) known as the Deconvolutional layer. A deconvolutional layer reverses the operation of a standard convolutional layer i.e. if the output generated through a standard convolutional layer is deconvolved, you get back the original input.

What is transposed convolution used for?

Transposed Convolutions are used to upsample the input feature map to a desired output feature map using some learnable parameters. The basic operation that goes in a transposed convolution is explained below: 1. Consider a 2x2 encoded feature map which needs to be upsampled to a 3x3 feature map.

How do you calculate transposed convolution?

Again, assuming square shaped tensors, the formula for transposed convolution is: Let's try this with Example 7, where the input size = 3, stride = 2, padding = 1, kernel size = 2. The calculation is then simply 2*2 - 2 + 1 + 1 = 4, so the output is of size 4.

Is transposed convolution same as deconvolution?

A transposed convolutional layer attempts to reconstruct the spatial dimensions of the convolutional layer and reverses the downsampling and upsampling techniques applied to it. A deconvolution is a mathematical operation that reverses the process of a convolutional layer.