  The most prominent problem associated with the deconvolution layer is the
presence of checkerboard artifacts in output images and dense labels. To combat
this problem, smoothness constraints, post processing and different
architecture designs have been proposed. Odena et al. highlight three sources
of checkerboard artifacts: deconvolution overlap, random initialization and
loss functions. In this note, we proposed an initialization method for
sub-pixel convolution known as convolution NN resize. Compared to sub-pixel
convolution initialized with schemes designed for standard convolution kernels,
it is free from checkerboard artifacts immediately after initialization.
Compared to resize convolution, at the same computational complexity, it has
more modelling power and converges to solutions with smaller test errors.
