hila-chefer / Transformer-MM-Explainability

[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.

Date Created 2021-03-23 (3 years ago)
Commits 77 (last one 2 years ago)
Stargazers 823 (0 this week)
Watchers 7 (0 this week)
Forks 107
License mit
Ranking

RepositoryStats indexes 612,937 repositories, of these hila-chefer/Transformer-MM-Explainability is ranked #62,763 (90th percentile) for total stargazers, and #273,269 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #1,340/18,242.

hila-chefer/Transformer-MM-Explainability is also tagged with popular topics, for these it's ranked: visualization (#316/1615),  transformer (#150/1044),  transformers (#162/870),  explainable-ai (#22/176),  interpretability (#25/172)

Other Information

hila-chefer/Transformer-MM-Explainability has 3 open pull requests on Github, 2 pull requests have been merged over the lifetime of the repository.

Github issues are enabled, there are 9 open issues and 27 closed issues.

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Watcher History

Github watchers over time, collection started in '23

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Recent Commit History

16 commits on the default branch (main) since jan '22

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Issue History

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updated: 2025-02-08 @ 03:58am, id: 350871478 / R_kgDOFOnftg