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clip-topk-sae

Safetensors
model_hub_mixin
pytorch_model_hub_mixin
sparse-autoencoder
clip
imagenet
interpretability
computer-vision
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  [![Paper](https://img.shields.io/badge/arXiv-2509.24510-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2509.24510)
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  [![Code](https://img.shields.io/badge/GitHub-ttt__theory-blue.svg?logo=github)](https://github.com/patrikwolf/ttt_theory)
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- [![Model](https://img.shields.io/badge/HuggingFace-SAE-orange.svg?logo=huggingface)](https://huggingface.co/patrikwolf/clip-sae)
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  This repository provides a **Top-K Sparse Autoencoder (SAE)** checkpoint trained on CLIP embeddings of ImageNet images. The model learns sparse, interpretable latent representations that enable better understanding of feature encoding in vision foundation models.
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  [![Paper](https://img.shields.io/badge/arXiv-2509.24510-b31b1b.svg?logo=arxiv)](https://arxiv.org/abs/2509.24510)
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  [![Code](https://img.shields.io/badge/GitHub-ttt__theory-blue.svg?logo=github)](https://github.com/patrikwolf/ttt_theory)
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+ <!-- [![Model](https://img.shields.io/badge/HuggingFace-SAE-orange.svg?logo=huggingface)](https://huggingface.co/patrikwolf/clip-topk-sae) -->
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  This repository provides a **Top-K Sparse Autoencoder (SAE)** checkpoint trained on CLIP embeddings of ImageNet images. The model learns sparse, interpretable latent representations that enable better understanding of feature encoding in vision foundation models.
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