Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
Aug 29, 2024 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Model interpretability and understanding for PyTorch
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)
A JAX research toolkit for building, editing, and visualizing neural networks.
moDel Agnostic Language for Exploration and eXplanation
XAI - An eXplainability toolbox for machine learning
ReFT: Representation Finetuning for Language Models
Interpretability Methods for tf.keras models with Tensorflow 2.x
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…
Public facing deeplift repo
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
👋 Xplique is a Neural Networks Explainability Toolbox
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions
Locating and editing factual associations in GPT (NeurIPS 2022)
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Fast SHAP value computation for interpreting tree-based models
Interpretability for sequence generation models 🐛 🔍
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