What is fractionally-strided convolution layer?
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.