The Top 223 Interpretability Open Source Projects on Github

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Code Quality 📦 28

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Computer Science 📦 74

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Content Management 📦 167

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Data Processing 📦 266

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Frameworks 📦 198

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Integrated Development Environments 📦 47

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Machine Learning 📦 336

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Mathematics 📦 55

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Software Architecture 📦 70

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User Interface 📦 310

User Interface Components 📦 465

Version Control 📦 29

Virtualization 📦 68

Web Browsers 📦 38

Web Servers 📦 25

Web User Interface 📦 194

The Top 223 Interpretability Open Source Projects on Github

Categories > Machine Learning > Interpretability

Shap ⭐ 14,794

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

Awesome Production Machine Learning ⭐ 10,439

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

Interpret ⭐ 4,322

Fit interpretable models. Explain blackbox machine learning.

Lucid ⭐ 4,232

A collection of infrastructure and tools for research in neural network interpretability.

Pytorch Grad Cam ⭐ 3,696

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Captum ⭐ 2,806

Model interpretability and understanding for PyTorch

Awesome Machine Learning Interpretability ⭐ 2,348

A curated list of awesome machine learning interpretability resources.

Stellargraph ⭐ 2,184

StellarGraph - Machine Learning on Graphs

Shapash ⭐ 1,425

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

Alibi ⭐ 1,395

Algorithms for explaining machine learning models

Awesome Federated Learning ⭐ 1,055

Federated Learning Library: https://fedml.ai

moDel Agnostic Language for Exploration and eXplanation

Tf Explain ⭐ 883

Interpretability Methods for tf.keras models with Tensorflow 2.x

XAI - An eXplainability toolbox for machine learning

Ad_examples ⭐ 678

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Grad Cam ⭐ 652

[ICCV 2017] Torch code for Grad-CAM

Xai_resources ⭐ 623

Interesting resources related to XAI (Explainable Artificial Intelligence)

Deeplift ⭐ 584

Public facing deeplift repo

Flashtorch ⭐ 581

Visualization toolkit for neural networks in PyTorch! Demo -->

Torch Cam ⭐ 561

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)

Transformers Interpret ⭐ 500

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

Code for the TCAV ML interpretability project

Interpretable_machine_learning_with_python ⭐ 472

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Neural Backed Decision Trees ⭐ 445

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Awesome Explainable Ai ⭐ 444

A collection of research materials on explainable AI/ML

Mli Resources ⭐ 405

H2O.ai Machine Learning Interpretability Resources

Decision Forests ⭐ 372

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

Human-explainable AI.

Imodels ⭐ 315

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

Aspect Based Sentiment Analysis ⭐ 295

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

Awesome_deep_learning_interpretability ⭐ 250

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

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

Transformer Mm Explainability ⭐ 207

[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.

Modelstudio ⭐ 206

📍 Interactive Studio for Explanatory Model Analysis

Explainx ⭐ 170

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

Awesome Fairness In Ai ⭐ 168

A curated list of awesome Fairness in AI resources

Visual Attribution ⭐ 117

Pytorch Implementation of recent visual attribution methods for model interpretability

Awesome Computer Vision ⭐ 115

Awesome Resources for Advanced Computer Vision Topics

Yggdrasil Decision Forests ⭐ 115

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

Lrp_for_lstm ⭐ 115

Layer-wise Relevance Propagation (LRP) for LSTMs

Adversarial Explainable Ai ⭐ 109

💡 Adversarial attacks on model explanations, and evaluation approaches

Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

Hierarchical Dnn Interpretations ⭐ 95

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

Visualizing Cnns For Monocular Depth Estimation ⭐ 94

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

⬛ Python Individual Conditional Expectation Plot Toolbox

Breakdown ⭐ 87

Model Agnostics breakDown plots

All about explainable AI, algorithmic fairness and more

Reverse Engineering Neural Networks ⭐ 74

A collection of tools for reverse engineering neural networks.

Deep Explanation Penalization ⭐ 74

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

For calculating global feature importance using Shapley values.

Ibreakdown ⭐ 63

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

Explanation_explorer ⭐ 62

A user interface to interpret machine learning models.

Warpedganspace ⭐ 61

Authors official PyTorch implementation of the "WarpedGANSpace: Finding non-linear RBF paths in GAN latent space" [ICCV 2021].

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

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Awesome Artificial Intelligence Research ⭐ 56

A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.

Trelawney ⭐ 55

General Interpretability Package

Interpretability By Parts ⭐ 55

[CVPR 2020 Oral] Interpretable and Accurate Fine-grained Recognition via Region Grouping

Representer_point_selection ⭐ 53

code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018

Conceptbottleneck ⭐ 50

Concept Bottleneck Models, ICML 2020

Bayesgrad ⭐ 48

BayesGrad: Explaining Predictions of Graph Convolutional Networks

Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.

Neuron Importance Zsl ⭐ 47

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

A Python library for Secure and Explainable Machine Learning

Text classification models. Used a submodule for other projects.

👋 Xplique is a Neural Networks Explainability Toolbox

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

Contrastiveexplanation ⭐ 37

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Thermostat ⭐ 37

Collection of NLP model explanations and accompanying analysis tools

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

Code/figures in Right for the Right Reasons

Xai Iml Sota ⭐ 33

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

Knowledge Neurons ⭐ 33

A library for finding knowledge neurons in pretrained transformer models.

Glcapsnet ⭐ 32

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

Adversarial Robustness Public ⭐ 30

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

Movie Recommendation Netflix ⭐ 29

🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧

Learning Finite State Representations of Recurrent Policy Networks

Fat Forensics ⭐ 28

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

Adaptive Wavelets ⭐ 25

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Fooling Lime Shap ⭐ 25

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

Removal Explanations ⭐ 24

A lightweight implementation of removal-based explanations for ML models.

Data generator for Arena - interactive XAI dashboard

Prototree ⭐ 23

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

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

Surrogate Assisted Feature Extraction in R

Disentangled Attribution Curves ⭐ 19

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Diabetes_use_case ⭐ 19

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Visualization tool for interpreting NLP models

Kernel Mod ⭐ 18

NeurIPS 2018. Linear-time model comparison tests.

Rulecovering ⭐ 17

Rule covering for interpretation and boosting

Concept Based Xai ⭐ 17

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

Interpretable Ml ⭐ 17

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

Wsol Min Max Entropy Interpretability ⭐ 16

Pytorch code for: Min-max Entropy for Weakly Supervised Pointwise Localization

Partial_dependence ⭐ 15

Python package to visualize and cluster partial dependence.

Fido Saliency ⭐ 15

Explaining Image Classifiers by Counterfactual Generation

Shapeshop ⭐ 15

Towards Understanding Deep Learning Representations via Interactive Experimentation

Oncotext ⭐ 14

OncoText is an information extraction service for breast pathology reports. It supports over 20 categories including DCIS, includes pretrained models, and supports flexible addition of new categories, new training data, and parsing new reports.

Machine Learning Summer Schools ⭐ 14

Curated materials for different machine learning related summer schools

Neural Ir Explorer ⭐ 14

Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results

Free Lunch Saliency ⭐ 13

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

1-100 of 223 projects

Related Projects

Python Interpretability Projects (111)

Machine Learning Interpretability Projects (104)

Jupyter Notebook Interpretability Projects (67)

Advertising 📦 9

Application Programming Interfaces 📦 120

Applications 📦 181

Artificial Intelligence 📦 72

Blockchain 📦 70

Build Tools 📦 111

Cloud Computing 📦 79

Code Quality 📦 28

Collaboration 📦 30

Command Line Interface 📦 48

Community 📦 81

Companies 📦 60

Compilers 📦 60

Computer Science 📦 74

Configuration Management 📦 39

Content Management 📦 167

Control Flow 📦 197

Data Formats 📦 77

Data Processing 📦 266

Data Storage 📦 132

Economics 📦 60

Frameworks 📦 198

Graphics 📦 103

Hardware 📦 148

Integrated Development Environments 📦 47

Learning Resources 📦 147

Libraries 📦 119

Lists Of Projects 📦 21

Machine Learning 📦 336

Mapping 📦 61

Marketing 📦 15

Mathematics 📦 55

Messaging 📦 97

Networking 📦 304

Operating Systems 📦 84

Operations 📦 120

Package Managers 📦 52

Programming Languages 📦 229

Runtime Environments 📦 96

Science 📦 42

Security 📦 375

Social Media 📦 26

Software Architecture 📦 70

Software Development 📦 68

Software Performance 📦 57

Software Quality 📦 127

Text Editors 📦 45

Text Processing 📦 131

User Interface 📦 310

User Interface Components 📦 465

Version Control 📦 29

Virtualization 📦 68

Web Browsers 📦 38

Web Servers 📦 25

Web User Interface 📦 194

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