mlsanityData to 14 Oct 2026

Style Transfer Dataset: What Makes A Good Stylization?

arXiv 2412.17139 ↗First posted 22 Dec 2024cs.CV eess.IVcited by 3

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Abstract (arXiv)

We present a new dataset with the goal of advancing image style transfer - the task of rendering one image in the style of another image. The dataset covers various content and style images of different size and contains 10.000 stylizations manually rated by three annotators in 1-10 scale. Based on obtained ratings, we find which factors are mostly responsible for favourable and poor user evaluations and show quantitative measures having statistically significant impact on user grades. A methodology for creating style transfer datasets is discussed. Presented dataset can be used in automating multiple tasks, related to style transfer configuration and evaluation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

9 of 15 references.

References · 9

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  3. 2021StyTr2: Image Style Transfer with Transformerscited by 187
  4. 2021AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfercited by 178
  5. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  6. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  7. 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
  8. 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
  9. 2022Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metriccited by 6

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Modelscited by 1
  2. 2025Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Modelscited by 1
  3. 2025LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfercited by 1