# Multiple Style Transfer via Variational AutoEncoder

Source: [Multiple Style Transfer via Variational AutoEncoder](<https://www.sota2.com/research/paper/68fa6b101e209131cea2bddc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:10:35.614Z

Authors: Zhi-Song Liu, Vicky Kalogeiton, Marie-Paule Cani

Published: 2021-10-13T06:47:13.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2110.07375>)

[PDF](<https://arxiv.org/pdf/2110.07375.pdf>)