RELAX: Representation Learning Explainability

This repository contains code for RELAX, a framework for representation learning explainability. RELAX is based on perturbation-based explainability and work by measuring the change in the representation space as parts of the input are masked out.

When should you use RELAX? If your output is a vector representation and you have no label information.

More information can be found in the paper: Representation Learning Explainability (Wickstrøm et al., 2023), (Wickstrøm et al., 2022)

You can see RELAX used in practice in medical image retrieval in the paper: A clinically motivated self-supervised approach for content-based image retrieval of CT liver images (Wickstrøm et al., 2023), (Wickstrøm et al., 2022)

Installation

RELAX can be installed using pip as follows:

pip install relax-xai

Citation

If you find RELAX interesting and use it in your research, use the following Bibtex annotation to cite:

@article{wickstrom2023relax,
  author  = {Wickstr\o{}m, Kristoffer K. and Trosten, Daniel J. and L\o{}kse, Sigurd and Boubekki, Ahc\`{e}ne and Mikalsen, Karl \o{}yvind and Kampffmeyer, Michael C. and Jenssen, Robert},
  title   = {RELAX: Representation Learning Explainability},
  journal = {International Journal of Computer Vision},
  year    = {2023},
  volume  = {131},
  number  = {6},
  pages   = {1584–1610},
  url     = {https://doi.org/10.1007/s11263-023-01773-2}
}