# Image Synthesis and Style Transfer

## TL;DR

This paper explores synthesizing new images using deep neural network representations, specifically from the VGG network, enabling creative image transformations through inverse mapping of learned features.

## Contribution

It introduces a method for image synthesis and style transfer based on inverting deep neural network representations, highlighting the potential for creative image manipulation.

## Key findings

- Generated images demonstrate effective style transfer.
- Distributed neural representations capture contours and shapes.
- Inverse mapping enables novel image synthesis.

## Abstract

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.