Image Modification Using Deep Neural Cellular Automata

EasyChair Preprint 7692

Abstract

Art Style Transfer is part of the rapidly growing AI Art community of recent times. Pioneered by Gatys et al, this class of methods makes it possible to convey styles, textures, patterns, and more. to a target image. The expressive masking feature of mainframe vision models such as VGG19 is used as a lossy function. The method of performing this transformation  takes many forms, from the original method that directly optimizes the image pixels to more recent forms that form the CNN form to create a generic transport network. The method presented in this article is similar to more recent methods, but takes advantage of a new class of deep learning methods,  deep neural automation. This new method provides the ability to convert any image into a target type that, like the CNN method mentioned previously, uses the same automatic data update rules over and over again. This paper contains how to use NCAs to transform images. also contains the Gatys et. al. type style transfer and the other a OpenAI CLIP based version where a prompt can be given to train NCAs to perform that transformation.

Keyphrases: Convolutional Neural Network, Neural Automata, cellular automata

@booklet{EasyChair:7692,
  author    = {Kaushal Gore and Chinmay Kokate and Sanskar Kothari and Koustubh Soman and Saurav Kamtalwar},
  title     = {Image Modification Using Deep Neural Cellular Automata},
  howpublished = {EasyChair Preprint 7692},
  year      = {EasyChair, 2022}}