114 Open source projects that are alternatives of or similar to adversarial-robustness-public

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

[ICCV 2017] Torch code for Grad-CAM

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

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

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

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

General Interpretability Package

[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)

Federated Learning Library: https://fedml.ai

📍 Interactive Studio for Explanatory Model Analysis

Model Agnostics breakDown plots

H2O.ai Machine Learning Interpretability Resources

Recentrifuge: robust comparative analysis and contamination removal for metagenomics

A collection of tools for reverse engineering neural networks.

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

💡 A curated list of adversarial attacks on model explanations

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

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

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

Interpretability Methods for tf.keras models with Tensorflow 2.x

A curated list of awesome machine learning interpretability resources.

Visualization toolkit for neural networks in PyTorch! Demo -->

NeurIPS 2018. Linear-time model comparison tests.

Code for the TCAV ML interpretability project

StellarGraph - Machine Learning on Graphs

⬛ Python Individual Conditional Expectation Plot Toolbox

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

Collection of NLP model explanations and accompanying analysis tools

Awesome Resources for Advanced Computer Vision Topics

A curated (most recent) list of resources for Learning with Noisy Labels

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

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

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

An Algorithm to Quantify Robustness of Recurrent Neural Networks

Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.

Model interpretability and understanding for PyTorch

Text classification models. Used a submodule for other projects.

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

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

A collection of research materials on explainable AI/ML

Algorithms for monitoring and explaining machine learning models

Data generator for Arena - interactive XAI dashboard

moDel Agnostic Language for Exploration and eXplanation

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

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 Convolutional Network.

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

XAI - An eXplainability toolbox for machine learning

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

Interesting resources related to XAI (Explainable Artificial Intelligence)

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Public facing deeplift repo

Layer-wise Relevance Propagation (LRP) for LSTMs

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

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

A curated list of awesome Fairness in AI resources

阿里云MNS

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

Minimal implementation of Denoised Smoothing (https://arxiv.org/abs/2003.01908) in TensorFlow.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Pytorch Implementation of recent visual attribution methods for model interpretability