mlsanityData to 14 Oct 2026

Neural Style Transfer: A Review

arXiv 1705.04058 ↗First posted 11 May 2017cs.CV cs.NE eess.IVcited by 199

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Abstract (arXiv)

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at https://github.com/ycjing/Neural-Style-Transfer-Papers.

Key facts. Cited by 199 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 May 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 33; 2022: 46; 2023: 43; 2024: 39; 2025: 23; 2026: 15.

199 papers.

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  1. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
  2. 2022Text2LIVE: Text-Driven Layered Image and Video Editingcited by 312
  3. 2022Splicing ViT Features for Semantic Appearance Transfercited by 209
  4. 2023StyleDrop: Text-to-Image Generation in Any Stylecited by 199
  5. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  6. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 155
  7. 2022ARF: Artistic Radiance Fieldscited by 147
  8. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 136
  9. 2024Implicit Style-Content Separation using B-LoRAcited by 135
  10. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  11. 2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
  12. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
38 more
  1. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  2. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  3. 2024CSGO: Content-Style Composition in Text-to-Image Generationcited by 78
  4. 2022WildNet: Learning Domain Generalized Semantic Segmentation from the Wildcited by 76
  5. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  6. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  7. 2024LightIt: Illumination Modeling and Control for Diffusion Modelscited by 57
  8. 2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
  9. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  10. 2021Rethinking and Improving the Robustness of Image Style Transfercited by 48
  11. 2022Federated Domain Generalization for Image Recognition via Cross-Client Style Transfercited by 40
  12. 2021Meta-Learning Dynamics Forecasting Using Task Inferencecited by 38
  13. 2021Language-Driven Image Style Transfercited by 37
  14. 2022Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replaycited by 35
  15. 2024GeoDiffuser: Geometry-Based Image Editing with Diffusion Modelscited by 34
  16. 2023DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Modelscited by 33
  17. 2021GANs Can Play Lottery Tickets Toocited by 32
  18. 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
  19. 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
  20. 2023Object-aware Inversion and Reassembly for Image Editingcited by 30
  21. 2022Learning Graph Neural Networks for Image Style Transfercited by 29
  22. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  23. 2024FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Modelscited by 22
  24. 2022AesUST: Towards Aesthetic-Enhanced Universal Style Transfercited by 21
  25. 2022MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfercited by 19
  26. 2022QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelitycited by 18
  27. 2024No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentationcited by 18
  28. 2023Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaboratecited by 17
  29. 2024Diffusion-Based Visual Art Creation: A Survey and New Perspectivescited by 17
  30. 2021Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalizationcited by 15
  31. 2022Landscape Learning for Neural Network Inversioncited by 15
  32. 2023LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesiscited by 15
  33. 2021Facial-Sketch Synthesis: A New Challengecited by 13
  34. 2022Unsupervised Model Adaptation for Source-free Segmentation of Medical Imagescited by 13
  35. 2023Extremal Domain Translation with Neural Optimal Transportcited by 12
  36. 2023Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attackscited by 12
  37. 2024Reference-based Controllable Scene Stylization with Gaussian Splattingcited by 12
  38. 2021DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfercited by 11

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