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155 search results found

Pytorch Grad Cam ⭐ 8,723

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

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Tensorwatch ⭐ 3,333

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Giskard ⭐ 2,509

🐢 The testing framework for ML models, from tabular to LLMs

High-Performance Symbolic Regression in Python and Julia

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Imodels ⭐ 1,229

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

Awesome Interpretable Machine Learning ⭐ 856

Iccv 2023 Papers ⭐ 806

ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

Lofo Importance ⭐ 785

Leave One Feature Out Importance

Xplique ⭐ 522

👋 Xplique is a Neural Networks Explainability Toolbox

Human-explainable AI.

Machine_learning_tutorials ⭐ 421

Code, exercises and tutorials of my personal blog ! 📝

Aspect Based Sentiment Analysis ⭐ 413

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

Datascience_artificialintelligence_utils ⭐ 369

Examples of Data Science projects and Artificial Intelligence use-cases

Tf Keras Vis ⭐ 286

Neural network visualization toolkit for tf.keras

Vit Explain ⭐ 260

Explainability for Vision Transformers

Interpretability for sequence generation models 🐛 🔍

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

Shapley ⭐ 198

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Explainable Cnn ⭐ 196

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Whitebox ⭐ 184

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

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

Hierarchical Dnn Interpretations ⭐ 119

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

Sagemaker Explaining Credit Decisions ⭐ 87

Amazon SageMaker Solution for explaining credit decisions.

Tsinterpret ⭐ 83

An Open-Source Library for the interpretability of time series classifiers

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"

Dialogue Understanding ⭐ 82

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

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

Pytorch_explain ⭐ 80

PyTorch Explain: Logic Explained Networks in Python.

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

A Python library that encapsulates various methods for neuron interpretation and analysis in Deep NLP models.

Pytorch implementation of "Explainable and Explicit Visual Reasoning over Scene Graphs "

Machine Learning ⭐ 68

Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.

Essential NLP & ML, short & fast pure Python code

Cinnamon ⭐ 66

CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system

Fat Forensics ⭐ 63

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

Da_visualization ⭐ 62

[CVPR2021] "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Semantically Structured Sentence Embeddings

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

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Information Bottlenecks for Attribution

Interpretability By Parts ⭐ 55

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

In the wild extraction of entities that are found using Flair and displayed using a very elegant front-end.

Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.

Xaitk Saliency ⭐ 48

An open source, Explainable AI (XAI) framework and toolkit for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.

Xplainable ⭐ 48

Real-time explainable machine learning for business optimisation

Prototree ⭐ 46

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

Neuro Symbolic Sudoku Solver ⭐ 44

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

Contrastiveexplanation ⭐ 42

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

Influenciae ⭐ 42

👋 Influenciae is a Tensorflow Toolbox for Influence Functions

Relational_deep_reinforcement_learning ⭐ 41

Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..

Explainablevqa ⭐ 41

[ACMMM Oral, 2023] "Towards Explainable In-the-wild Video Quality Assessment: A Database and a Language-Prompted Approach"

Torchprism ⭐ 40

Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework

WikiWhy is a new benchmark for evaluating LLMs' ability to explain between cause-effect relationships. It is a QA dataset containing 9000+ "why" question-answer-rationale triplets.

Concept Based Xai ⭐ 37

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

Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox

Color_distillation ⭐ 33

Codes for CVPR 2020 paper "Learning to Structure an Image with Few Colors". Critical structure for network recognition. #explainable-ai

Yolo Heatmaps ⭐ 32

A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).

Demo code for the paper: OccAM's Laser

Global Attribution Mapping ⭐ 31

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Model Agnostic Counterfactual Explanations

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"

Bert_attn_viz ⭐ 25

Visualize BERT's self-attention layers on text classification tasks

Te2rules ⭐ 25

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Xai_thyroid ⭐ 24

All Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images

Learning Bottleneck Concepts in Image Classification

Patho Gan ⭐ 24

Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"

Sdk Python ⭐ 23

Python library for Modzy Machine Learning Operations (MLOps) Platform

Disentangled Attribution Curves ⭐ 23

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

This package implements the trustee framework to extract decision tree explanation from black-box ML models.

Neatly packaged AI methods for explainable ECG analysis

Imle Net ⭐ 20

Official implementation of our IEEE:SMC 2021 paper "IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification"

MTUNet: Few-shot Image Classification with Visual Explanations

GitHub repository for DORA: Data-agnOstic Representation Analysis paper. DORA allows to find outlier representations in Deep Neural Networks.

Pytorch Lattice ⭐ 18

A PyTorch implementation of constrained optimization and modeling techniques

Relative_attributing_propagation ⭐ 18

Interpreting DNNs, Relative attributing propagation

a framework for Automated Classification of Medical Images

Pyceterisparibus ⭐ 17

Python library for Ceteris Paribus Plots (What-if plots)

This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).

Protopformer ⭐ 17

ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition

Explainable Machine Learning through Contextual Importance and Utility

Xai Demonstrator ⭐ 17

The XAI Demonstrator is a modular platform that lets users interact with production-grade Explainable AI (XAI) systems.

Fused Window Transformers for fMRI Time Series Analysis (https://www.sciencedirect.com/science/article/pii

Lightx3ecg ⭐ 16

Dlime_experiments ⭐ 16

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Gnn Subnet ⭐ 16

Disease subnetwork detection with explainable Graph Neural Networks

Modeling Uncertainty Local Explainability ⭐ 16

Local explanations with uncertainty 💐!

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

Pyexplainer ⭐ 15

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

Stexplainer ⭐ 15

"STExplainer: Explainable Spatio-Temporal Graph Neural Networks"

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

Health Fact Checking ⭐ 15

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

Cellx Predict ⭐ 15

Explainable AI model of cell behavior

Library for generating human-readable machine learning explanations

A Toolbox for the Evaluation of machine learning Explanations

MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.

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