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

? 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

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)