Popular GitHub Repositories for Fool Lime

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

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

Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification

A PyTorch Toolbox for creating adversarial examples that fool neural networks.

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

:skull: A collection of methods to fool the deep neural network :skull:

Overview of different model interpretability libraries.

This project focuses on detecting deepfake audio using advanced neural network architectures like VGG16, MobileNet, ResNet, and custom CNNs. It incorporates explainable AI (XAI) methods like LIME, Grad-CAM, and SHAP to enhance detection accuracy and provide insights into model predictions.

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

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).

This repository is the official implementation of [Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks (NeurIPS'22)](https://arxiv.org/abs/2210.02041).

General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems

An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.

[ICML 2024] Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

Causality-Aware Local Interpretable Model Agnostic Explanations

Implementation of the sped-up solver of LIME image enhancement algorithm from the paper "LIME: Low-Light Image Enhancement via Illumination Map Estimation"

A platform that provides users with easy access to AI services developed by Montimage and usage of explainable AI techniques (e.g., LIME, SHAP).