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

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

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

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Open-source framework to detect outliers in Elasticsearch events

Image Mosaicing or Panorama Creation

Deep Learning for Anomaly Deteection

Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.

Beyond Outlier Detection: LookOut for Pictorial Explanation

[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

A python package for robust statistics

One-class classifiers for anomaly detection (outlier detection)

Implementation of the Robust Random Cut Forest algorithm for anomaly detection

This repository contains the code, data, and models from the paper Vladan Stojnić, Zakaria Laskar, Giorgos Tolias, "Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning", In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.

In statistics, an outlier is an observation point that is distant from other observations. How we can filter out these values using python?

Supervised machine learning methods for novel anomaly detection.

Single Cell Outlier Selector - quickly find outliers in your single-cell data

Feature Engineering konulu bir kursun içeriğini ve materyallerini barındırmaktadır. Kurs, veri bilimi ve makine öğrenmesi alanında temel bir konu olan "özellik mühendisliği"ni ele almaktadır.

Useful tools for statistical data exploration.