captum

inseq-team/inseq

Interpretability for sequence generation models 🐛 🔍

DFKI-NLP/thermostat

Collection of NLP model explanations and accompanying analysis tools

copenlu/ALPS_2021

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

TannerGilbert/Model-Interpretation

Overview of different model interpretability libraries.

robinvanschaik/interpret-flair

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

richouzo/hate-speech-detection-survey

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

LuanAdemi/VisualGo

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

esceptico/toxic

End-to-end toxic Russian comment classification

nicovandenhooff/indoor-scene-detector

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

tsKenneth/interpretable-graph-classification

Interpretable graph classifications using Graph Convolutional Neural Network

speediedan/deep_classiflie

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 deems most likely to be labeled falsehoods, the @DeepClassiflie twitter bot tweets out a statement analysis and model interpretation "report"

the-ahuja-lab/Odorify-webserver

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

braindatalab/xai-tris

XAI-Tris

jihyeonseong/SAI-board-by-streamlit

Cyber Security AI Dashboard

dg1223/explainable-ai

Model interpretability for Explainable Artificial Intelligence

js-yoo/xai_kimst2020

"XAI를 위한 Attribution Method 접근법 분석 및 동향 Analysis and Trend of Attribution Methods for XAI" 에서 사용한 코드와 예시를 공개

speediedan/deep_classiflie_db

Deep_classiflie_db is the backend data system for managing Deep Classiflie metadata, analyzing Deep Classiflie intermediate datasets and orchestrating Deep Classiflie model training pipelines. Deep_classiflie_db includes data scraping modules for the initial model data sources. Deep Classiflie depends upon deep_classiflie_db for much of its analytical and dataset generation functionality but the data system is currently maintained as a separate repository here to maximize architectural flexibility. Depending on how Deep Classiflie evolves (e.g. as it supports distributed data stores etc.), it may make more sense to integrate deep_classiflie_db back into deep_classiflie. Currently, deep_classiflie_db releases are synchronized to deep_classiflie releases. To learn more, visit deepclassiflie.org.

manyue-zhang/Frontend-for-Captum

Based on the papers "Interpretability Beyond Feature Attribution: QuantitativeTestingwithConceptActivationVectors(TCAV)" and Captum's instantiation https://captum.ai/docs/captum_insights, we developed this frontend for the Captum project based on the streamlit framework.

R-N/covid-forecasting-joint-learning

COVID-19 forecasting model for East Java cities using Joint Learning. My undergrad thesis.

LennardZuendorf/thesis-files

Collection of associated files for my bachelor thesis

ProGamerGov/captum-tutorials

yuneg11/Interpretability-Metrics

Interpretability Metrics