79 Open source projects that are alternatives of or similar to Xai_resources

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

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Python package to visualize and cluster partial dependence.

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A curated list of awesome Fairness in AI resources

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

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

A lightweight implementation of removal-based explanations for ML models.

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

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML 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.

Awesome Resources for Advanced Computer Vision Topics

For calculating global feature importance using Shapley values.

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

Human-explainable AI.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

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

Model interpretability and understanding for PyTorch

H2O.ai Machine Learning Interpretability Resources

A Python package implementing a new machine learning model for text classification with visualization tools for Explainable AI

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

Layer-wise Relevance Propagation (LRP) for LSTMs

Towards Understanding Deep Learning Representations via Interactive Experimentation

⬛ Python Individual Conditional Expectation Plot Toolbox

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

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.

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

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

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

Fit interpretable models. Explain blackbox machine learning.

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

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

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

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

NeurIPS 2018. Linear-time model comparison tests.

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

Collection of NLP model explanations and accompanying analysis tools

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

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

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

Public facing deeplift repo

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

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

A curated list of awesome machine learning interpretability resources.

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

📍 Interactive Studio for Explanatory Model Analysis

Concept Bottleneck Models, ICML 2020

StellarGraph - Machine Learning on Graphs

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Pytorch Implementation of recent visual attribution methods for model interpretability

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Model Agnostics breakDown plots

A library for finding knowledge neurons in pretrained transformer models.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

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

Code for the TCAV ML interpretability project

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)

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

Molecular Explanation Generator