mlsanityData to 7 Oct 2026

Multiple Style Transfer via Variational AutoEncoder

arXiv 2110.07375 ↗First posted 13 Oct 2021cs.CV eess.IVcited by 9

Not on the map. Why?

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 13 Oct 2021.

Builds on

23 of 29 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  5. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  6. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  7. 2017Universal Style Transfer via Feature Transformscited by 352
  8. 2018Diverse Image-to-Image Translation via Disentangled Representationscited by 296
  9. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  10. 2017Deep Photo Style Transfercited by 168
  11. 2020Adversarial Latent Autoencoderscited by 155
  12. 2018A Closed-form Solution to Photorealistic Image Stylizationcited by 133
11 more
  1. 2016Controlling Perceptual Factors in Neural Style Transfercited by 99
  2. 2017Action Tubelet Detector for Spatio-Temporal Action Localizationcited by 77
  3. 2017Multi-style Generative Network for Real-time Transfercited by 61
  4. 2017Diversified Texture Synthesis with Feed-forward Networkscited by 59
  5. 2018Learning Linear Transformations for Fast Arbitrary Style Transfercited by 48
  6. 2018Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptationcited by 18
  7. 2019Image Super-Resolution via Attention based Back Projection Networkscited by 16
  8. 2020Unsupervised Real Image Super-Resolution via Generative Variational AutoEncodercited by 16
  9. 2020From Paris to Berlin: Discovering Fashion Style Influences Around the Worldcited by 10
  10. 2020Distribution Aligned Multimodal and Multi-Domain Image Stylizationcited by 10
  11. 2019Multiple Style-Transfer in Real-Timecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 2; 2026: 1.

9 papers.

  1. 2022The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Modelscited by 24
  2. 2022Name Your Style: An Arbitrary Artist-aware Image Style Transfercited by 14
  3. 2023Breaching FedMD: Image Recovery via Paired-Logits Inversion Attackcited by 7
  4. 2024StyleMamba : State Space Model for Efficient Text-driven Image Style Transfercited by 5
  5. 2024SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Style Transfercited by 2
  6. 2025Leveraging Diffusion Models for Stylization using Multiple Style Imagescited by 1
  7. 2024Bridging Text and Image for Artist Style Transfer via Contrastive Learningcited by 0
  8. 2025Cloud Optical Thickness Retrievals Using Angle Invariant Attention Based Deep Learning Modelscited by 0
  9. 2026Abstract-LoRA: Unlocking Single-Image Style Transfer through Targeted U-Net Block Trainingcited by 0