captum

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

Interpretability for sequence generation models 🐛 🔍

Collection of NLP model explanations and accompanying analysis tools

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

Overview of different model interpretability libraries.

A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.

Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).

We introduce XBrainLab, an open-source user-friendly software, for accelerated interpretation of neural patterns from EEG data based on cutting-edge computational approach.

SOTA Time Series Interpretability Library for PyTorch, from Supervised to Foundation Models

Cyber Security AI Dashboard

End-to-end toxic Russian comment classification

Training a CNN to recognize the current Go position with photorealistic renders

Robustness analysis of post-hoc XAI explanations under adversarial perturbations, covering SHAP, LIME, and Integrated Gradients across phishing, intrusion detection, and fraud datasets, with four attack strategies and hybrid defense achieving 91.1% explanation drift reduction.

This repository contains the source code for Indoor Scene Detector, a full stack deep learning computer vision application.

Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model d…

XAI-Tris

Model interpretability for Explainable Artificial Intelligence

Interpretable graph classifications using Graph Convolutional Neural Network

🔍 Enhance medical imaging with a lightweight CNN model that offers over 91% accuracy and integrated explainability for better clinical trust.

OdoriFy is an open-source tool with multiple prediction engines. This is the source code of the webserver.