interpretability

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

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.

A collection of infrastructure and tools for research in neural network interpretability.

A curated list of awesome responsible machine learning resources.

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

StellarGraph - Machine Learning on Graphs

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, Finer-CAM, LeGrad, RefineCAM)

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

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

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

[ICCV 2017] Torch code for Grad-CAM

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

[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.

Locating and editing factual associations in GPT (NeurIPS 2022)

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

Interpretability for sequence generation models 🐛 🔍