signxai2 0.15.1

Extended variant of ZeNNit with SIGNed explanations - Unveiling relevant features by reducing bias

SIGN (Sign-based Improvement of Gradient-based explaNations) is a novel XAI method intended to reduce bias in explanations that are intrinsically induced by several state-of-the-art XAI methods. The SIGN-XAI-2 package enables simple application of this method in your projects using the established Zennit (Zennit explains neural networks in torch) package pypi.org/project/zennit/.

If you use this package or parts of it in your own work, please consider citing our paper:

 @article{Gumpfer2023SIGN,
    title = {SIGNed explanations: Unveiling relevant features by reducing bias},
    author = {Nils Gumpfer and Joshua Prim and Till Keller and Bernhard Seeger and Michael Guckert and Jennifer Hannig},
    journal = {Information Fusion},
    pages = {101883},
    year = {2023},
    issn = {1566-2535},
    doi = {https://doi.org/10.1016/j.inffus.2023.101883}
}

Documentation

The latest documentation is available from timexaigroup.github.io/signxai2.

Install

To install the package directly from PyPI using pip, use:

$ pip install signxai2

Usage

SIGN-XAI-2 is based on Zennit and works with PyTorch. If you want to know more about Zennit and its usage, visit github.com/chr5tphr/zennit. There is also a version of SIGN available for usage with TensorFlow environments pypi.org/project/signxai/.

Example Code:

import signxai2

For more details and examples, have a look at our documentation.

License

SIGN-XAI-2 and Zennit are licensed under the GNU LESSER GENERAL PUBLIC LICENSE VERSION 3 OR LATER -- see the COPYING and COPYING.LESSER files for details.