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Unified Style Transfer

arXiv 2110.10481 ↗First posted 20 Oct 2021cs.CVcited by 0

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Abstract (arXiv)

Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer. In this paper, a novel approach, the Unified Style Transfer (UST) model, is proposed. With the introduction of a generative model for internal style representation, UST can transfer images in two approaches, i.e., Domain-based and Image-based, simultaneously. At the same time, a new philosophy based on the human sense of art and style distributions for evaluating the transfer model is presented and demonstrated, called Statistical Style Analysis. It provides a new path to validate style transfer models' feasibility by validating the general consistency between internal style representation and art facts. Besides, the translation-invariance of AdaIN features is also discussed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 20 Oct 2021.

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22 of 34 references.

References · 22

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  5. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
  6. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  7. 2015A Neural Algorithm of Artistic Stylecited by 480
  8. 2017Universal Style Transfer via Feature Transformscited by 352
  9. 2016A Learned Representation For Artistic Stylecited by 340
  10. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  11. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  12. 2015Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Featurecited by 181
10 more
  1. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  2. 2018Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decorationcited by 115
  3. 2017StyleBank: An Explicit Representation for Neural Image Style Transfercited by 89
  4. 2020Collaborative Distillation for Ultra-Resolution Universal Style Transfercited by 52
  5. 2019Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfercited by 36
  6. 2017XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappingscited by 27
  7. 2020Unity Style Transfer for Person Re-Identificationcited by 17
  8. 2016Convolutional Sketch Inversioncited by 4
  9. 2018Learning from Multi-domain Artistic Images for Arbitrary Style Transfercited by 4
  10. 2019Artist Style Transfer Via Quadratic Potentialcited by 1

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