# Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2310.18237 · HTML: https://mlsanity.com/paper/2310.18237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway](https://mlsanity.com/paper/1411.6422/) (2014)
- [One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1706.00826/) (2017)
- [Deep Feature Interpolation for Image Content Changes](https://mlsanity.com/paper/1611.05507/) (2016)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning](https://mlsanity.com/paper/2304.04839/) (2023)
- [A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample](https://mlsanity.com/paper/1910.02192/) (2019)

## Built on by (most cited first, 2 of 2)

- [ART3D: 3D Gaussian Splatting for Text-Guided Artistic Scenes Generation](https://mlsanity.com/paper/2405.10508/) (2024)
- [Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms](https://mlsanity.com/paper/2311.05435/) (2023)

## Abstract (arXiv)

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content