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UAE

This repository contains the weights for Unified Autoencoding (UAE), introduced in the paper The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding.

TypeOtherVersionEp lastBClasse

Source du modèle

Type
Other
Taille du fichier
1,50 MB–12,8 GB
Version
vEp last
Mis à jour
22 oct. 2026
Source
Hugging Face

Description de la source

The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding

This repository contains the weights for Unified Autoencoding (UAE), introduced in the paper The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding.

Introduction

Unified Autoencoding (UAE) is a novel model architecture that harmonizes semantic structure and pixel details via an innovative frequency-band modulator, enabling their seamless coexistence. It is based on the "Prism Hypothesis," which suggests that different data modalities (semantic vs. pixel) can be viewed as projections of the natural world onto a shared feature spectrum. UAE effectively unifies semantic abstraction and pixel-level fidelity within a single latent space, achieving state-of-the-art performance in both reconstruction and representation learning.

Citation

@misc{fan2025uae,
      title={The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding}, 
      author={Weichen Fan and Haiwen Diao and Quan Wang and Dahua Lin and Ziwei Liu},
      year={2025},
      eprint={2512.19693},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.19693}, 
}

Sources

1 source
Hugging FacePrincipalVersion cee9a34c26a73c08496a7e93511879596faf4965
huggingface.co/weepiess2383/UAE/tree/cee9a34c26a73c08496a7e93511879596faf4965

Vérifié 22 oct.

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Artefacts du modèle

9 artefacts

checkpoints/CLIP-L/ep-last.pt

pt · 10,8 GB · SHA-256 13b3c2e828cb…8881 · Hugging Face

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checkpoints/DINOv2-B/ep-last.pt

pt · 7,56 GB · SHA-256 bf7ede54f633…c538 · Hugging Face

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checkpoints/Generative_Modeling/Latent_Modeling/64_tokens/normalization_stats.pt

pt · 1,50 MB · SHA-256 952b1b766405…a500 · Hugging Face

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checkpoints/Generative_Modeling/Latent_Modeling/64_tokens/stage_1.pt

pt · 7,56 GB · SHA-256 bf7ede54f633…c538 · Hugging Face

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checkpoints/Generative_Modeling/Latent_Modeling/64_tokens/stage_2.pt

pt · 12,5 GB · SHA-256 06f6dd323966…f66a · Hugging Face

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checkpoints/Generative_Modeling/Pixel_Modeling/checkpoint-100.pth

pth · 12,8 GB · SHA-256 689e97762f29…845d · Hugging Face

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checkpoints/SIGLIP2/ep-last.pt

pt · 7,60 GB · SHA-256 7316aaf5a653…fef2 · Hugging Face

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discs/dino_vit_small_patch8_224.pth

pth · 82,7 MB · SHA-256 6f9f986e17ef…3a7b · Hugging Face

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stats/64_tokens/normalization_stats.pt

pt · 1,50 MB · SHA-256 952b1b766405…a500 · Hugging Face

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Extraits de sources

2 extraits

--- license: apache-2.0 pipeline_tag: image-feature-extraction --- # The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding This repository contains the weights for **Unified Autoencoding (UAE)**, introduced in the paper [The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding](https://arxiv.org/abs/2512.19693). - **Github Repository:** https://github.com/WeichenFan/UAE - **Paper:** [arXiv:2512.19693](https://arxiv.org/abs/2512.19693) ## Introduction Unified Autoencoding (UAE) is a novel model architecture that harmonizes semantic structure and pixel details via an innovative frequency-band modulator, enabling their seamless coexistence. It is based on the "Prism Hypothesis," which suggests that different data modalities (semantic vs. pixel) can be viewed as projections of the natural world onto a shared feature spectrum. UAE effectively unifies semantic abstraction and pixel-level fidelity within a single latent space, achieving state-of-the-art performance in both reconstruction and representation learning. ## Citation ```bibtex @misc{fan2025uae, title={The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding}, author={Weichen Fan and Haiwen Diao and Quan Wang and Dahua Lin and Ziwei Liu}, year={2025}, eprint={2512.19693}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2512.19693}, } ```

Source context: 0 downloads · 3 likes · Pipeline image-feature-extraction · Repo weepiess2383/UAE