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 about a year ago)

Stargazers 734 (3 this week)

Watchers 8 (0 this week)

Forks 105

License mit

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RepositoryStats indexes 534,551 repositories, of these hila-chefer/Transformer-MM-Explainability is ranked #64,300 (88th percentile) for total stargazers, and #236,072 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #1,316/14,929.

hila-chefer/Transformer-MM-Explainability is also tagged with popular topics, for these it's ranked: visualization (#323/1472), transformer (#147/916), transformers (#145/732), explainable-ai (#22/154), interpretability (#26/145)

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 8 open issues and 27 closed issues.

All Topics

vqa clip detr lxmert visualbert transformer transformers visualization explainability explainable-ai interpretability

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

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updated: 2024-06-28 @ 10:35am, id: 350871478 / R_kgDOFOnftg