90 Open source projects that are alternatives of or similar to removal-explanations

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

For calculating global feature importance using Shapley values.

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

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

Data generator for Arena - interactive XAI dashboard

Collection of NLP model explanations and accompanying analysis tools

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Fit interpretable models. Explain blackbox machine learning.

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data

Fast approximate Shapley values in R

Python package to visualize and cluster partial dependence.

Visualization tool for Graph Neural Networks

NeurIPS 2018. Linear-time model comparison tests.

A Julia package for interpretable machine learning with stochastic Shapley values

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

Model interpretability and understanding for PyTorch

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

Multi-Touch Attribution

Concept Bottleneck Models, ICML 2020

A collection of research materials on explainable AI/ML

🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

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

Code for "Free-Lunch Saliency via Attention in Atari Agents"

Papers about explainability of GNNs

A curated list of awesome machine learning interpretability resources.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

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

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

Towards Understanding Deep Learning Representations via Interactive Experimentation

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Layer-wise Relevance Propagation (LRP) for LSTMs

StellarGraph - Machine Learning on Graphs

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

A curated list of awesome Fairness in AI resources

📍 Interactive Studio for Explanatory Model Analysis

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Explaining the output of machine learning models with more accurately estimated Shapley values

Pytorch Implementation of recent visual attribution methods for model interpretability

⬛ Python Individual Conditional Expectation Plot Toolbox

Model Agnostics breakDown plots

Molecular Explanation Generator

Awesome Resources for Advanced Computer Vision Topics

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

A library for finding knowledge neurons in pretrained transformer models.

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network