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Transformer-MM-Explainability
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.
★912stars
Jupyter Notebook
MIT
Updated: 6d ago
📋 Project at a Glance
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What's this?A open-source Security & Governance project, built with Jupyter Notebook, focusing on clip and detr
Who made it?Maintained by hila-chefer team, 912⭐ on GitHub, #137 out of 413 in Security & Governance
Why does it exist?The hila-chefer team recognized that existing clip tools in Security & Governance were hard to use. Transformer-MM-Explainability was designed to make detr more accessible.
What can it do?Key use cases: explainability, explainable-ai, interpretability
How to install with AI?Use an AI coding assistant to follow the README and automatically handle the install and environment setup.
🔗 github.com/hila-chefer/Transformer-MM-Explainability
🔗 github.com/hila-chefer/Transformer-MM-Explainability
Topics
clipdetrexplainabilityexplainable-aiinterpretabilitylxmerttransformertransformersvisualbertvisualizationvqa