Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Interpretability and explainability of data and machine learning models

Algorithms for explaining machine learning models

Generate Diverse Counterfactual Explanations for any machine learning model.

XAI - An eXplainability toolbox for machine learning

moDel Agnostic Language for Exploration and eXplanation

Layer-wise Relevance Propagation (LRP) for LSTMs.

Repository for the Explainable Deep One-Class Classification paper

👋 Xplique is a Neural Networks Explainability Toolbox

Neural network visualization toolkit for tf.keras

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Machine Learning in one line of code

A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

As part of the Explainable AI Toolkit (XAITK), XAITK-Saliency is an open source, explainable AI framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.

Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.