Explainability14 / 45 projects

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

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.

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

The nnsight package enables interpreting and manipulating the internals of deep learned models.

[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)

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

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

A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:

Diffusers-Interpret 🤗🧨🕵️‍♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.

Repository for the Explainable Deep One-Class Classification paper

Repository for "BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation", accepted at EAMT 2023.