88 Open source projects that are alternatives of or similar to Captum

🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

For calculating global feature importance using Shapley values.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

Python package to visualize and cluster partial dependence.

Code for "Free-Lunch Saliency via Attention in Atari Agents"

A Julia package for interpretable machine learning with stochastic Shapley values

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

Concept Bottleneck Models, ICML 2020

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Molecular Explanation Generator

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

[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.

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Data generator for Arena - interactive XAI dashboard

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

NeurIPS 2018. Linear-time model comparison tests.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

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

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