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lrp

Here are 31 public repositories matching this topic...

Tensorflow tutorial for various Deep Neural Network visualization techniques

  • UpdatedAug 22, 2020
  • Jupyter Notebook

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

  • UpdatedJul 19, 2024
  • Python

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

  • UpdatedFeb 19, 2021
  • Python

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

  • UpdatedJun 12, 2024
  • Jupyter Notebook
  • UpdatedMar 29, 2018
  • Python

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

  • UpdatedNov 2, 2022
  • Python

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

  • UpdatedJan 9, 2025
  • Jupyter Notebook

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

  • UpdatedSep 24, 2019
  • Python

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

  • UpdatedAug 7, 2022
  • Python

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

  • UpdatedMar 28, 2022
  • Jupyter Notebook

[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.

  • UpdatedJan 25, 2023
  • Python

xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

  • UpdatedMar 3, 2025
  • Jupyter Notebook

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

  • UpdatedMar 8, 2024
  • Python

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

  • UpdatedAug 20, 2024
  • Jupyter Notebook

We predict religion from personal names only.

  • UpdatedSep 17, 2024
  • Jupyter Notebook

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.

  • UpdatedMay 31, 2021
  • Jupyter Notebook

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