# MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer

arXiv: https://arxiv.org/abs/2211.15313 · HTML: https://mlsanity.com/paper/2211.15313/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 66 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 30 of 47)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Contrastive Learning for Compact Single Image Dehazing](https://mlsanity.com/paper/2104.09367/) (2021)
- [CLIPstyler: Image Style Transfer with a Single Text Condition](https://mlsanity.com/paper/2112.00374/) (2021)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization](https://mlsanity.com/paper/2203.07740/) (2022)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows](https://mlsanity.com/paper/2103.16877/) (2021)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Arbitrary Video Style Transfer via Multi-Channel Correlation](https://mlsanity.com/paper/2009.08003/) (2020)
- [Dynamic Instance Normalization for Arbitrary Style Transfer](https://mlsanity.com/paper/1911.06953/) (2019)
- [Arbitrary Style Transfer via Multi-Adaptation Network](https://mlsanity.com/paper/2005.13219/) (2020)
- [A Style-Aware Content Loss for Real-time HD Style Transfer](https://mlsanity.com/paper/1807.10201/) (2018)
- [Collaborative Distillation for Ultra-Resolution Universal Style Transfer](https://mlsanity.com/paper/2003.08436/) (2020)

## Built on by (most cited first, 19 of 19)

- [StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models](https://mlsanity.com/paper/2308.07863/) (2023)
- [SGSST: Scaling Gaussian Splatting StyleTransfer](https://mlsanity.com/paper/2412.03371/) (2024)
- [Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers](https://mlsanity.com/paper/2304.11335/) (2023)
- [DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification](https://mlsanity.com/paper/2412.09224/) (2024)
- [SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer](https://mlsanity.com/paper/2503.15934/) (2025)
- [SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models](https://mlsanity.com/paper/2508.03402/) (2025)
- [AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2312.05928/) (2023)
- [TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer](https://mlsanity.com/paper/2309.06004/) (2023)
- [Scaling Painting Style Transfer](https://mlsanity.com/paper/2212.13459/) (2022)
- [Lightweight texture transfer based on texture feature preset](https://mlsanity.com/paper/2306.16846/) (2023)
- [Color and Texture Dual Pipeline Lightweight Style Transfer](https://mlsanity.com/paper/2310.01321/) (2023)
- [Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation](https://mlsanity.com/paper/2412.05296/) (2024)
- [HSI: A Holistic Style Injector for Arbitrary Style Transfer](https://mlsanity.com/paper/2502.04369/) (2025)
- [RLMiniStyler: Light-weight RL Style Agent for Arbitrary Sequential Neural Style Generation](https://mlsanity.com/paper/2505.04424/) (2025)
- [RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13672/) (2023)
- [Dual Pipeline Style Transfer with Input Distribution Differentiation](https://mlsanity.com/paper/2311.05432/) (2023)
- [ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model](https://mlsanity.com/paper/2405.15287/) (2024)
- [Pluggable Style Representation Learning for Multi-Style Transfer](https://mlsanity.com/paper/2503.20368/) (2025)
- [Contrastive-Augmented Flow Matching for Style-Content Disentanglement](https://mlsanity.com/paper/2607.12404/) (2026)

## Abstract (arXiv)

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.