# Exploring the structure of a real-time, arbitrary neural artistic stylization network

arXiv: https://arxiv.org/abs/1705.06830 · HTML: https://mlsanity.com/paper/1705.06830/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2017.

## Built on by (most cited first, 30 of 89)

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- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
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- [Styleformer: Transformer based Generative Adversarial Networks with Style Vector](https://mlsanity.com/paper/2106.07023/) (2021)
- [FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation](https://mlsanity.com/paper/2206.00050/) (2022)
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- [Adaptive Denoising via GainTuning](https://mlsanity.com/paper/2107.12815/) (2021)
- [DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data](https://mlsanity.com/paper/2306.14153/) (2023)
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## Abstract (arXiv)

In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.