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alibi

Algorithms for explaining machine learning models

augmentor

Image augmentation library in Python for machine learning.

beexplainable

XAI Experiments on an Annotated Dataset of Wild Bee Images

calibrated_explanations

Repository for the explanation method Calibrated Explanations (CE)

conceptbottleneck

Concept Bottleneck Models, ICML 2020

craft

cxplain

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

deformable-protopnet

The official repository for Deformable ProtoPNet, as described in "Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes".

docker

3 Step tutorial how to use docker

geometric-smote

Implementation of the Geometric SMOTE over-sampling algorithm.

git-commands

A list of commonly used Git commands

google_landmark_classifier_2019

Building a ResNet50-Classifier in TensorFlow-Keras to tackle Google's classification challenge

influenciae

👋 Influenciae is a Tensorflow Toolbox for Influence Functions

innvestigate

A toolbox to iNNvestigate neural networks' predictions!

interpretability-by-parts

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

lang-segment-anything

SAM with text prompt

mmal-net

This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).

modeling-uncertainty-local-explainability

Local explanations with uncertainty 💐!

nlxgpt

NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks, CVPR 2022 (Oral)

predicting_bike_rentals

Bike sharing dataset analyzed with Multilinear Regression, Random Forests and Time Series

protopnet

This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).

protopool

Code for "Interpretable image classification with differentiable prototypes assignment", ECCV 2022

prototree

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

pytorch-forecasting

Time series forecasting with PyTorch

quantus

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

taylor_app

An app to aproximate important functions using the Taylor series

tcav

Merged repositories for TCAV and ACE

teaser

TEASER - Tool for Energy Analysis and Simulation for Efficient Retrofit

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