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.

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

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

Pytorch implementation of various neural network interpretability methods

Explainable AI in Julia.

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

No description provided.

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

Implementation or LRP and Object detection on Brain scans to detect Brain Tumor and Alzhimers

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

xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

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

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

[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.

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

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

Cyber Security AI Dashboard

We predict religion from personal names only.

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

Simulate directional sound – deep neural network (DNN) – layer-wise relevance propagation (LRP)

ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

Repository for the 'best student paper award' winning paper at the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022), Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography, Mahbub Ul Alam, Jón Rúnar Baldvinsson and Yuxia Wang. https://doi.org/10.1109/CBMS55023.2022.00052

Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'

This repository contains the code to generate the questionnaire that was conducted for the sake of our paper *Labarta et al.: Study on the Helpfulness of Explainable Artificial Intelligence (2024)* as well as the scripts for the analysis of the gathered survey results.

Code used in paper 'Comprehensive social trait judgments from faces in autism spectrum disorder'

(Master's Thesis) Alam, Mahbub Ul, From Speech to Image: A Novel Approach to Understand the Hidden Layer Mechanisms of Deep Neural Networks in Automatic Speech Recognition, Masterarbeit, Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart, 2017. (https://www.ims.uni-stuttgart.de/en/research/publications/theses/)

No description provided.

An attempt at implementing the Layerwise Relevance Propagation in a CUDA runtime