# hila-chefer/Transformer-MM-Explainability

<https://repo.masters-masters.com/en/repos/hila-chefer/Transformer-MM-Explainability>

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

hila-chefer/Transformer-MM-Explainability has 914 stars on GitHub. Its RepoMasters card: Beta tier. \[ICCV 2021- Oral\] Official PyTorch implementation for…

| Field | Value |
| --- | --- |
| Owner | [hila-chefer](https://repo.masters-masters.com/en/owners/hila-chefer) |
| Stars | 914 |
| Forks | 118 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | MIT |
| Created | 2021 |
| Topics | transformers, transformer, vqa, detr, visualization, explainability, explainable-ai, interpretability, lxmert, visualbert |
| Tier | [Beta (700 to 1,999 ★)](https://repo.masters-masters.com/en/ranks/box_office) |
| View on GitHub | [hila-chefer/Transformer-MM-Explainability](https://github.com/hila-chefer/Transformer-MM-Explainability) |

## Repository

\[ICCV 2021- Oral\] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

The tier for repos with 700 to 1,999 ★. Published odds per card pulled: 6%. Rarity: Rare.

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