113 Open source projects that are alternatives of or similar to meg

[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.

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

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

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

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

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

A research library for Deep Graph Networks

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)

Fit interpretable models. Explain blackbox machine learning.

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

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

📍 Interactive Studio for Explanatory Model Analysis

StellarGraph - Machine Learning on Graphs

Pytorch Implementation of recent visual attribution methods for model interpretability

Examples of Data Science projects and Artificial Intelligence use cases

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

Model Agnostics breakDown plots

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

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

A collection of tools for reverse engineering neural networks.

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

Data generator for Arena - interactive XAI dashboard

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

💡 A curated list of adversarial attacks on model explanations

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

NeurIPS 2018. Linear-time model comparison tests.

Layer-wise Relevance Propagation (LRP) for LSTMs

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

A curated list of awesome Fairness in AI resources

Collection of NLP model explanations and accompanying analysis tools

⬛ Python Individual Conditional Expectation Plot Toolbox

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

Awesome Resources for Advanced Computer Vision Topics

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

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

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

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

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

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

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).

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

Text classification models. Used a submodule for other projects.

General Interpretability Package

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

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

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

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

Algorithms for monitoring and explaining machine learning models

Model interpretability and understanding for PyTorch

[ICCV 2017] Torch code for Grad-CAM

moDel Agnostic Language for Exploration and eXplanation

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

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.

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

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.

A collection of research materials on explainable AI/ML

Federated Learning Library: https://fedml.ai