#Outlier-detection
Showing 23 of 23 repositories tagged #outlier-detection, ranked by stars
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 anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
List of tools & datasets for anomaly detection on time-series data.
fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
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)
ELKI Data Mining Toolkit
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.
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
HandySpark - bringing pandas-like capabilities to Spark dataframes
A multi-agent framework to fully automate anomaly detection in different modalities, tabular, graph, time series, and more (work in progress)!
Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)
Data preprocessing is a data mining technique that involves transforming raw data into an understandable format.
Contains all my data science projects.