TE2Rules

A game theoretic approach to explain the output of any machine learning model.

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Algorithms for explaining machine learning models

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

A collection of research papers and software related to explainability in graph machine learning.

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

A JAX research toolkit for building, editing, and visualizing neural networks.

Interpretability and explainability of data and machine learning models

[ICCV 2017] Torch code for Grad-CAM

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.