# Multiple Style Transfer via Variational AutoEncoder

arXiv: https://arxiv.org/abs/2110.07375 · HTML: https://mlsanity.com/paper/2110.07375/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Adversarial Latent Autoencoders](https://mlsanity.com/paper/2004.04467/) (2020)
- [A Closed-form Solution to Photorealistic Image Stylization](https://mlsanity.com/paper/1802.06474/) (2018)
- [Controlling Perceptual Factors in Neural Style Transfer](https://mlsanity.com/paper/1611.07865/) (2016)
- [Action Tubelet Detector for Spatio-Temporal Action Localization](https://mlsanity.com/paper/1705.01861/) (2017)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [Diversified Texture Synthesis with Feed-forward Networks](https://mlsanity.com/paper/1703.01664/) (2017)
- [Learning Linear Transformations for Fast Arbitrary Style Transfer](https://mlsanity.com/paper/1808.04537/) (2018)
- [Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation](https://mlsanity.com/paper/1807.09384/) (2018)
- [Image Super-Resolution via Attention based Back Projection Networks](https://mlsanity.com/paper/1910.04476/) (2019)
- [Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder](https://mlsanity.com/paper/2004.12811/) (2020)
- [From Paris to Berlin: Discovering Fashion Style Influences Around the World](https://mlsanity.com/paper/2004.01316/) (2020)
- [Distribution Aligned Multimodal and Multi-Domain Image Stylization](https://mlsanity.com/paper/2006.01431/) (2020)
- [Multiple Style-Transfer in Real-Time](https://mlsanity.com/paper/1911.06464/) (2019)

## Built on by (most cited first, 9 of 9)

- [The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models](https://mlsanity.com/paper/2204.02641/) (2022)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack](https://mlsanity.com/paper/2304.11436/) (2023)
- [StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer](https://mlsanity.com/paper/2405.05027/) (2024)
- [SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Style Transfer](https://mlsanity.com/paper/2404.15743/) (2024)
- [Leveraging Diffusion Models for Stylization using Multiple Style Images](https://mlsanity.com/paper/2508.12784/) (2025)
- [Bridging Text and Image for Artist Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2410.09566/) (2024)
- [Cloud Optical Thickness Retrievals Using Angle Invariant Attention Based Deep Learning Models](https://mlsanity.com/paper/2505.24638/) (2025)
- [Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Training](https://mlsanity.com/paper/2609.13239/) (2026)

## Abstract (arXiv)

Modern works on style transfer focus on transferring style from a single image. Recently, some approaches study multiple style transfer; these, however, are either too slow or fail to mix multiple styles. We propose ST-VAE, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, enabling to merge styles via linear interpolation before transferring the new style to the content image. To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. We also present a case study revealing that ST-VAE outperforms other methods while being faster, flexible, and setting a new path for multiple style transfer.