# Training on Art Composition Attributes to Influence CycleGAN Art Generation

## TL;DR

This paper introduces a method to guide CycleGAN image translation by integrating an Art Composition Attributes Network trained on art evaluation rules, enhancing control over generated art images.

## Contribution

The paper presents a novel approach of using an auxiliary neural network trained on art composition attributes to influence CycleGAN outputs.

## Key findings

- ACAN effectively encodes art composition attributes.
- Incorporating ACAN improves control over CycleGAN translation.
- Method leverages art domain knowledge for better image synthesis.

## Abstract

I consider how to influence CycleGAN, image-to-image translation, by using additional constraints from a neural network trained on art composition attributes. I show how I trained the the Art Composition Attributes Network (ACAN) by incorporating domain knowledge based on the rules of art evaluation and the result of applying each art composition attribute to apple2orange image translation.