Tensorflow tutorial for various Deep Neural Network visualization techniques

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

A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

Pytorch implementation of various neural network interpretability methods

Explainable AI in Julia.

A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).

使用LSTM及股票因子数据预测未来收益，使用LRP(layer-wise relevance propagation)增强网络可解释性

[ECCV 2022: Oral] In this work, we discover that color is a crtical transferable forensic feature (T-FF) in universal detectors for detecting CNN-generated images.

Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.

Using Explainable Artificial Intelligence (XAI) for sentiment analysis (NLP)

An XAI library that helps to explain AI models in a really quick & easy way

Explainability of Deep RL algorithms using graph networks and layer-wise relevance propagation.

We predict religion from personal names only.

Cyber Security AI Dashboard

Implementation of explainability algorithms (layer-wise relevance propagation, local interpretable model-agnostic explanations, gradient-weighted class activation mapping) on computer vision architectures to identify and explain regions of COVID 19 pneumonia in chest X-ray and CT scans.

Transfer Explainability via Layer-Wise Relevance Propagation Demo for AAAI

xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology