mlsanityData to 14 Oct 2026

Arbitrary Style Transfer with Deep Feature Reshuffle

arXiv 1805.04103 ↗First posted 10 May 2018cs.CVcited by 75

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Abstract (arXiv)

This paper introduces a novel method by reshuffling deep features (i.e., permuting the spacial locations of a feature map) of the style image for arbitrary style transfer. We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods. This simple idea can effectively address the challenging issues in existing style transfer methods. On one hand, it can avoid distortions in local style patterns, and allow semantic-level transfer, compared with neural parametric methods. On the other hand, it can preserve globally similar appearance to the style image, and avoid wash-out artifacts, compared with neural non-parametric methods. Based on the proposed loss, we also present a progressive feature-domain optimization approach. The experiments show that our method is widely applicable to various styles, and produces better quality than existing methods.

Key facts. Cited by 75 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 19; 2023: 18; 2024: 13; 2025: 7; 2026: 3.

75 papers.

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  1. 2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 212
  2. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  3. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Modelscited by 155
  4. 2022ARF: Artistic Radiance Fieldscited by 147
  5. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  6. 2022Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learningcited by 93
  7. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 67
  8. 20213D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Imagecited by 45
  9. 2021Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfercited by 41
  10. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
  11. 2021Style-Aware Normalized Loss for Improving Arbitrary Style Transfercited by 30
  12. 2022SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonizationcited by 24
38 more
  1. 2022Neural Neighbor Style Transfercited by 23
  2. 2024StylizedGS: Controllable Stylization for 3D Gaussian Splattingcited by 23
  3. 2021Exemplar-Based 3D Portrait Stylizationcited by 21
  4. 2022AesUST: Towards Aesthetic-Enhanced Universal Style Transfercited by 21
  5. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  6. 2022QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelitycited by 18
  7. 2024StyleSplat: 3D Object Style Transfer with Gaussian Splattingcited by 18
  8. 2022Style-ERD: Responsive and Coherent Online Motion Style Transfercited by 17
  9. 2023Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaboratecited by 17
  10. 2022All-to-key Attention for Arbitrary Style Transfercited by 16
  11. 2024Geometry Transfer for Stylizing Radiance Fieldscited by 16
  12. 2021Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalizationcited by 15
  13. 2022S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attentioncited by 14
  14. 2024StyleTex: Style Image-Guided Texture Generation for 3D Modelscited by 14
  15. 2022Consistent Style Transfercited by 13
  16. 2021Texture Reformer: Towards Fast and Universal Interactive Texture Transfercited by 12
  17. 2024Reference-based Controllable Scene Stylization with Gaussian Splattingcited by 12
  18. 2021DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfercited by 11
  19. 2024CCA: Collaborative Competitive Agents for Image Editingcited by 10
  20. 2022Depth-aware Neural Style Transfer using Instance Normalizationcited by 9
  21. 2023Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfercited by 9
  22. 2021Deep Translation Prior: Test-time Training for Photorealistic Style Transfercited by 8
  23. 2022Controllable Style Transfer via Test-time Training of Implicit Neural Representationcited by 8
  24. 2021Neural Photometry-guided Visual Attribute Transfercited by 6
  25. 2022Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metriccited by 6
  26. 2022Semi-parametric Makeup Transfer via Semantic-aware Correspondencecited by 5
  27. 2022WISE: Whitebox Image Stylization by Example-based Learningcited by 5
  28. 2024G-Style: Stylized Gaussian Splattingcited by 5
  29. 2024DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfercited by 5
  30. 2023ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Modelscited by 4
  31. 2024Evaluation in Neural Style Transfer: A Reviewcited by 4
  32. 2021Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generationcited by 3
  33. 2022StyleFlow For Content-Fixed Image to Image Translationcited by 3
  34. 2023SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspectivecited by 3
  35. 2023Instant Photorealistic Neural Radiance Fields Stylizationcited by 3
  36. 2023A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applicationscited by 3
  37. 2023MuVieCAST: Multi-View Consistent Artistic Style Transfercited by 3
  38. 2023Painterly Image Harmonization by Learning from Painterly Objectscited by 3

50 most cited of 75 All 75 (JSON)