Popular GitHub Repositories for Outlier Ensembles

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Anomaly detection related books, papers, videos, and toolboxes

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

List of tools & datasets for anomaly detection on time-series data.

Algorithms for outlier, adversarial and drift detection

TODS: An Automated Time-series Outlier Detection System

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

A Python Library for Graph Outlier Detection (Anomaly Detection)

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Benchmarking Generalized Out-of-Distribution Detection

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

ML powered analytics engine for outlier detection and root cause analysis.

A Deep Graph-based Toolbox for Fraud Detection

Curated list of open source tooling for data-centric AI on unstructured data.

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Deep learning-based outlier/anomaly detection