---
license: cc-by-nc-sa-4.0
task_categories:
- image-feature-extraction
tags:
- interpretability
- vision-transformer
- sparse-autoencoders
- mechanistic-interpretability
---

# ViSAE: Neuroscience-Motivated Probing Suite

This dataset is part of the **ViSAE** toolbox, presented in the paper [Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers](https://huggingface.co/papers/2606.06664) (ICML 2026).

ViSAE is a mechanistic interpretability toolbox designed to decompose Vision Transformer (ViT) representations into human-interpretable concepts using Sparse Autoencoders (SAEs).

- **GitHub Repository:** [deep-real/ViSAE](https://github.com/deep-real/ViSAE)
- **Project Page:** [https://tangli0305.github.io/](https://tangli0305.github.io/)

## Dataset Summary

The probing suite consists of:
1.  **Probing Image Set:** 64,000 images designed for SAE training, optimized for high concept coverage (20x more efficient than ImageNet).
2.  **Concept Set:** A 16,000 visually grounded concept vocabulary for SAE feature interpretation (available in the GitHub repository).

## Usage

To use this dataset for training SAEs or tracing concept circuits, please refer to the official [GitHub repository](https://github.com/deep-real/ViSAE) for the implementation details, including:
- Extracting intermediate representations from ViTs.
- Training SAEs for each ViT layer.
- Mapping learned features to concepts using CLIP.

## Citation

```bibtex
@inproceedings{li2026visae,
 title={Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers},
 author={Li, Tang and Chen, Yanlin and Ma, Mengmeng and Peng, Xi},
 booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
 year={2026}
}
```