24 Open source projects that are alternatives of or similar to spark-lof

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Repository for detecting outliers using Grubb's Threshold & Generalized Extreme Studentized Deviate (ESD) Test

A Julia package for robust regressions using M-estimators and quantile regressions

Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"

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.

Repo of paper title 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X

TOD: GPU-accelerated Outlier Detection via Tensor Operations

Code for reproducing f-AnoGAN in Pytorch

Main component extraction for outlier detection

A scikit-learn compatible library for anomaly detection

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

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Anomaly detection related books, papers, videos, and toolboxes

No description or website provided.

(Python, R, C++) Explainable outlier/anomaly detection through decision tree conditioning

Fairness regulator and rate limiter

Supplementary material for KDD 2018 workshop "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles"

Arduino library for identifying outliers with a Hampel filter

ADRepository: Real-world anomaly detection datasets

VisualQC : assistive tool to ease the quality control workflow of neuroimaging data.

Solve many kinds of least-squares and matrix-recovery problems

Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection

DDC-Outlier: Preventing medication errors using unsupervised learning