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Notebooks for Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

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Summit Notebooks

Summit is an interactive system that scalably and systematically summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. This repository contains the python notebooks used to generate the data used in the Summit visualization.

For the main Summit repo, go to https://github.com/fredhohman/summit.

Main notebooks:

Experimental notebooks:

Live Demo

For a live demo, visit: fredhohman.com/summit

Resources

We used the following ImageNet metadata:

License

MIT License. See LICENSE.md.

Citation

@article{hohman2020summit,
  title={Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations},
  author={Hohman, Fred and Park, Haekyu and Robinson, Caleb and Chau, Duen Horng},
  journal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  year={2020},
  publisher={IEEE},
  url={https://fredhohman.com/summit/}
}

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For questions or support open an issue or contact Fred Hohman.

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Notebooks for Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

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