  Affine transformation, layer blending, and artistic filters are popular
processes that graphic designers employ to transform pixels of an image to
create a desired effect. Here, we examine various approaches that synthesize
new images: pixel-based compositing models and in particular, distributed
representations of deep neural network models. This paper focuses on
synthesizing new images from a learned representation model obtained from the
VGG network. This approach offers an interesting creative process from its
distributed representation of information in hidden layers of a deep VGG
network i.e., information such as contour, shape, etc. are effectively captured
in hidden layers of neural networks. Conceptually, if $\Phi$ is the function
that transforms input pixels into distributed representations of VGG layers
${\bf h}$, a new synthesized image $X$ can be generated from its inverse
function, $X = \Phi^{-1}({\bf h})$. We describe the concept behind the
approach, present some representative synthesized images and style-transferred
image examples.
