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Here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

Here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

And here is an animation of fractionally-strided convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]

Here is an animation of fractionally-strided convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]

Also, here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

And here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

Here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

Here is a post by Paul-Louis Pröve on different types of convolutions.

Here are two quotes from the post:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

And here is an animation of fractionally-strided convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]

Here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

Here are two quotes from a post by Paul-Louis Pröve on different types of convolutions:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

And here is an animation of fractionally-strided convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]

Here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

Here is a post by Paul-Louis Pröve on different types of convolutions.

Here are two quotes from the post:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

And here is an animation of Transposed Convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]

Here is a post on this site asking "What are deconvolutional layers?" which is the same thing.

Here is a post by Paul-Louis Pröve on different types of convolutions.

Here are two quotes from the post:

Transposed Convolutions (a.k.a. deconvolutions or fractionally strided convolutions)

and

Some sources use the name deconvolution, which is inappropriate because it’s not a deconvolution [..] An actual deconvolution reverts the process of a convolution.

And here is an animation of fractionally-strided convolution (from this github project):

where the dashed white cells are zero rows/columns padded between the input cells (blue). These animations are visualizations of the mathematical formulas from the article below:

A guide to convolution arithmetic for deep learning

Here is a quote from the article:

Figure [..] helps understand what fractional strides involve: zeros are inserted between input units, which makes the kernel move around at a slower pace than with unit strides [footnote: doing so is inefficient and real-world implementations avoid useless multiplications by zero, but conceptually it is how the transpose of a strided convolution can be thought of.]