# ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

arXiv: https://arxiv.org/abs/2103.09776 · HTML: https://mlsanity.com/paper/2103.09776/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [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)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [ArtEmis: Affective Language for Visual Art](https://mlsanity.com/paper/2101.07396/) (2021)
- [Exploring the structure of a real-time, arbitrary neural artistic stylization network](https://mlsanity.com/paper/1705.06830/) (2017)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [Artistic style transfer for videos](https://mlsanity.com/paper/1604.08610/) (2016)
- [Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval](https://mlsanity.com/paper/1604.04994/) (2016)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer](https://mlsanity.com/paper/1612.01895/) (2016)
- [OmniArt: Multi-task Deep Learning for Artistic Data Analysis](https://mlsanity.com/paper/1708.00684/) (2017)
- [From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators](https://mlsanity.com/paper/1603.02003/) (2016)
- [Visualizing and Understanding Deep Texture Representations](https://mlsanity.com/paper/1511.05197/) (2015)

## Built on by (most cited first, 30 of 34)

- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Measuring Style Similarity in Diffusion Models](https://mlsanity.com/paper/2404.01292/) (2024)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Evaluating Data Attribution for Text-to-Image Models](https://mlsanity.com/paper/2306.09345/) (2023)
- [StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models](https://mlsanity.com/paper/2409.02543/) (2024)
- [Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video](https://mlsanity.com/paper/2308.10305/) (2023)
- [Data Attribution for Text-to-Image Models by Unlearning Synthesized Images](https://mlsanity.com/paper/2406.09408/) (2024)
- [Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis](https://mlsanity.com/paper/2304.10278/) (2023)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [CoGS: Controllable Generation and Search from Sketch and Style](https://mlsanity.com/paper/2203.09554/) (2022)
- [StyleBabel: Artistic Style Tagging and Captioning](https://mlsanity.com/paper/2203.05321/) (2022)
- [EKILA: Synthetic Media Provenance and Attribution for Generative Art](https://mlsanity.com/paper/2304.04639/) (2023)
- [Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation](https://mlsanity.com/paper/2210.17398/) (2022)
- [Simple Disentanglement of Style and Content in Visual Representations](https://mlsanity.com/paper/2302.09795/) (2023)
- [HyperNST: Hyper-Networks for Neural Style Transfer](https://mlsanity.com/paper/2208.04807/) (2022)
- [U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI](https://mlsanity.com/paper/2406.15386/) (2024)
- [A LoRA is Worth a Thousand Pictures](https://mlsanity.com/paper/2412.12048/) (2024)
- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [PARASOL: Parametric Style Control for Diffusion Image Synthesis](https://mlsanity.com/paper/2303.06464/) (2023)
- [DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer](https://mlsanity.com/paper/2307.04157/) (2023)
- [SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models](https://mlsanity.com/paper/2508.03402/) (2025)
- [NeAT: Neural Artistic Tracing for Beautiful Style Transfer](https://mlsanity.com/paper/2304.05139/) (2023)
- [StyleProtect: Safeguarding Artistic Identity in Fine-tuned Diffusion Models](https://mlsanity.com/paper/2509.13711/) (2025)
- [ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer](https://mlsanity.com/paper/2304.05755/) (2023)
- [Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations](https://mlsanity.com/paper/2409.08245/) (2024)
- [IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features](https://mlsanity.com/paper/2412.14432/) (2024)
- [StyleDecoupler: Generalizable Artistic Style Disentanglement](https://mlsanity.com/paper/2601.17697/) (2026)
- [Self-supervised Multi-view Disentanglement for Expansion of Visual Collections](https://mlsanity.com/paper/2302.02249/) (2023)
- [Identifying Prompted Artist Names from Generated Images](https://mlsanity.com/paper/2507.18633/) (2025)
- [Leveraging Diffusion Models for Stylization using Multiple Style Images](https://mlsanity.com/paper/2508.12784/) (2025)

## Abstract (arXiv)

We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work.