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

Awesome Resources for Advanced Computer Vision Topics

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

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

A collection of tools for reverse engineering neural networks.

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

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

💡 A curated list of adversarial attacks on model explanations

Text classification models. Used a submodule for other projects.

General Interpretability Package

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

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

Algorithms for monitoring and explaining machine learning models

[ICCV 2017] Torch code for Grad-CAM

moDel Agnostic Language for Exploration and eXplanation

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

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.

Federated Learning Library: https://fedml.ai

XAI - An eXplainability toolbox for machine learning

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

Interesting resources related to XAI (Explainable Artificial Intelligence)

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

Public facing deeplift repo

Code for the TCAV ML interpretability project

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

H2O.ai Machine Learning Interpretability Resources

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

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)

Human-explainable AI.

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

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