Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
Interpreting medical image data with Alibi: Using Counterfactual RL in Kaggle Diabetic Retinopathy Dataset.
Revised the code at the link below from Tensorflow version to Pytorch version (+ additional revision w/r/t MNISTEncoder, MNISTDecoder so that AE applies to RGB image)
Run "xai.ipynb" file for model training.
BibTeX entry:
@article{JMLR:v22:21-0017,
author = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
title = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
journal = {Journal of Machine Learning Research},
year = {2021},
volume = {22},
number = {181},
pages = {1-7},
url = {http://jmlr.org/papers/v22/21-0017.html}
}