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Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers

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Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers

This is the official code for the paper "Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers" by Lukas Kuhn, Sari Sadiya, Joerg Schloetterer, Christin Seifert, Gemma Roig.

Please contact Lukas Kuhn (lukas.kuhn@dkfz-heidelberg.de) for any questions.

We opted for a Jupyter Notebook approach to make the code more accessible and to provide a more interactive experience, since an important part of our research is the understanding and visiualization of the detected shortcut for better interpretability by a domain expert.

The ISIC dataset used for this example can be downloaded here: ISIC Dataset

To install the required packages, please run pip install -r requirements.txt.

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Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers

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