Cleanlab's open-source library is 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

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

A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

TODS: An Automated Time-series Outlier Detection System

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

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

ELKI Data Mining Toolkit

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

A python library for time-series smoothing and outlier detection in a vectorized way.

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.

Deep learning-based outlier/anomaly detection

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)

SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].