moDel Agnostic Language for Exploration and eXplanation
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Updated
May 22, 2024 - Python
moDel Agnostic Language for Exploration and eXplanation
A generalized gradient-based CNN visualization technique
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks
SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
An Open-Source Library for the interpretability of time series classifiers
This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
Concept activation vectors for Keras
Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
Local interpretability for survival models
A Deepfake detector based on hybrid EfficientNet CNN and Vision Transformer archietcture. The model is explainable by rendering a heatmap visualization of the Transformer Relevancy / Attention map.
This tool can be used to find the most influential words on a document. We define most influential as the words that influence a trained classifier the most to give it a particular classification.
Local Universal Rule-based Explanations
Explanations in Multi-Model Planning
Similarity Differences and Uniqueness Explainable AI method
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