codebook-features

A game theoretic approach to explain the output of any machine learning model.

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

The Open Source Feature Store for AI/ML

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Algorithms for explaining machine learning models

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

A JAX research toolkit for building, editing, and visualizing neural networks.

[ICCV 2017] Torch code for Grad-CAM

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

Interpretability Methods for tf.keras models with Tensorflow 2.x