Here is [a post on this site][1] asking "What are deconvolutional layers?" which is the same thing.

Here are two quotes from [a post by Paul-Louis Pröve][2] 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][3]):

<img src="https://i.sstatic.net/2aSir.gif" width="300" />

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][4]

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.]


  [1]: https://datascience.stackexchange.com/q/6107/67328
  [2]: https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d
  [3]: https://github.com/vdumoulin/conv_arithmetic
  [4]: https://arxiv.org/pdf/1603.07285.pdf