# hila-chefer/RobustViT

<https://repo.masters-masters.com/en/repos/hila-chefer/RobustViT>

[Home](https://repo.masters-masters.com/en) › [hila-chefer](https://repo.masters-masters.com/en/owners/hila-chefer) › [RobustViT](https://repo.masters-masters.com/en/repos/hila-chefer/RobustViT)

hila-chefer/RobustViT has 134 stars on GitHub. Its RepoMasters card: Prototype tier. \[NeurIPS 2022\] Official PyTorch implementation of Optimizing Relevance…

| Field | Value |
| --- | --- |
| Owner | [hila-chefer](https://repo.masters-masters.com/en/owners/hila-chefer) |
| Stars | 134 |
| Forks | 14 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | None |
| Created | 2022 |
| Topics | explainability, robustness, vision-transformer, neurips, neurips-2022 |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [hila-chefer/RobustViT](https://github.com/hila-chefer/RobustViT) |

## Repository

\[NeurIPS 2022\] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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