Metadata-Version: 2.1
Name: tshield-xai
Version: 0.0.1
Summary: T-SHIELD Regularization for Artificial Intelligence
Author: Iván Sevillano Garcia
Author-email: Ivan Sevillano-Garcia <isevillano@ugr.es>
Project-URL: Homepage, https://github.com/isega24/SHIELD
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: scikit-learn
Requires-Dist: scikit-image
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: torchvision
Requires-Dist: efficientnet_pytorch
Requires-Dist: tqdm
Requires-Dist: opencv-python
Requires-Dist: IPython
Requires-Dist: seaborn
Requires-Dist: plotly
Requires-Dist: ipywidgets
Requires-Dist: sphinx
Requires-Dist: sphinx_rtd_theme
Requires-Dist: sphinxcontrib.bibtex
Requires-Dist: nbsphinx
Requires-Dist: wget
Requires-Dist: pandoc
Requires-Dist: revel-xai
Requires-Dist: torchsummary
Requires-Dist: tensorboard
Requires-Dist: twine

# T-SHIELD regularization

T-SHIELD (Transformation-Selective Hidden Input Evaluation for Learning Dynamics) is a regularization
technique that aims to enhance model interpretability while improves model performance. Specifically, T-SHIELD adds a regularization term to the objective function that penalizes if it relies too heavily on
a small subset of input features.

You can find the documentation of the package [here](https://tshield-xai.readthedocs.io/en/latest/).

If you use this code or find it useful, please cite our paper:

```

@article{sevillano2024shield,
  title={SHIELD: A regularization technique for eXplainable Artificial Intelligence},
  author={Sevillano-Garc{\'\i}a, Iv{\'a}n and Luengo, Juli{\'a}n and Herrera, Francisco},
  journal={arXiv preprint arXiv:2404.02611},
  year={2024}
}

```

## Installation

You can install this package by simply use pip:

```bash
pip install tshield-xai
```
