111 Open source projects that are alternatives of or similar to adaptive-wavelets

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

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

Fit interpretable models. Explain blackbox machine learning.

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

Data generator for Arena - interactive XAI dashboard

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

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

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

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

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

Papers about explainability of GNNs

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

Visualization tool for Graph Neural Networks

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

[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.

A curated list of awesome machine learning interpretability resources.

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

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

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

For calculating global feature importance using Shapley values.

Collection of NLP model explanations and accompanying analysis tools

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

StellarGraph - Machine Learning on Graphs

[ICCV 2017] Torch code for Grad-CAM

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

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

Pytorch Implementation of recent visual attribution methods for model interpretability

Federated Learning Library: https://fedml.ai

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

Layer-wise Relevance Propagation (LRP) for LSTMs

Algorithms for monitoring and explaining machine learning models

Model interpretability and understanding for PyTorch

moDel Agnostic Language for Exploration and eXplanation

A curated list of awesome Fairness in AI resources

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.

Machine Learning in one line of code

XAI - An eXplainability toolbox for machine learning

⬛ Python Individual Conditional Expectation Plot Toolbox

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

Interesting resources related to XAI (Explainable Artificial Intelligence)

A collection of research materials on explainable AI/ML

Model Agnostics breakDown plots

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

Public facing deeplift repo

Awesome Resources for Advanced Computer Vision Topics

Code for the TCAV ML interpretability project

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

CVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)

MindsDB server allows you to consume and expose MindsDB workflows, through http.

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

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

H2O.ai Machine Learning Interpretability Resources

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

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

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 machine learning model for text classification with visualization tools for Explainable AI

A collection of tools for reverse engineering neural networks.

Human-explainable AI.

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

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease