| --- |
| 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} |
| } |
| ``` |
