maxdreyer

Reveal2Revise

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. Paper presented at MICCAI 2023 conference.

L-CRP

Concept Relevance Propagation for Localization Models, accepted at SAIAD workshop at CVPR 2023.

crp-human-study

Repository with files and scripts for the human study of the CRP (Concept-Relevance-Propagation) paper

pcx

Prototypical Concept-based Explanations, accepted at SAIAD workshop at CVPR 2024.

PURE

Repository for PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits, accepted at CVPR 2024 XAI4CV Workshop (spotlight)

ACEPyTorch

Towards Automatic Concept-based Explanations

ConceptWhitening

deeplabv3

PyTorch implementation of DeepLabV3, trained on the Cityscapes dataset.

dissect

Code for the Proceedings of the National Academy of Sciences 2020 article, "Understanding the Role of Individual Units in a Deep Neural Network"

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

OpenOOD

Benchmarking Generalized Out-of-Distribution Detection

segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

zennit

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

zennit-crp

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization