126 Open source projects that are alternatives of or similar to xai-iml-sota

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

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

Fit interpretable models. Explain blackbox machine learning.

A curated list of awesome machine learning interpretability resources.

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RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

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A Julia package for interpretable machine learning with stochastic Shapley values

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Model verification, validation, and error analysis

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

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

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Pytorch Implementation of recent visual attribution methods for model interpretability

⬛ Python Individual Conditional Expectation Plot Toolbox

Model Agnostics breakDown plots

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Semi-automatic tool for manual segmentation of multi-spectral and geo-spatial imagery.

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A collection of tools for reverse engineering neural networks.

A curated list of awesome Fairness in AI resources

No description or website provided.

Collection of NLP model explanations and accompanying analysis tools

Awesome Resources for Advanced Computer Vision Topics

DataCLUE: 数据为中心的NLP基准和工具包

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

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

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

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

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

PatZilla is a modular patent information research platform and data integration toolkit with a modern user interface and access to multiple data sources.

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

💡 A curated list of adversarial attacks on model explanations

Text classification models. Used a submodule for other projects.

Model interpretability and understanding for PyTorch

General Interpretability Package

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

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Visualization tool for Graph Neural Networks

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

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

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Algorithms for monitoring and explaining machine learning models

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[ICCV 2017] Torch code for Grad-CAM

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Machine Learning in one line of code

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

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

Federated Learning Library: https://fedml.ai