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A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

Anomaly Detection Resources ⭐ 7,616

Anomaly detection related books, papers, videos, and toolboxes

Anomalib ⭐ 2,796

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

A machine learning library for detecting anomalies in signals.

Adbench ⭐ 609

Official Implement of "ADBench: Anomaly Detection Benchmark".

Handson Unsupervised Learning ⭐ 604

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

Padim Anomaly Detection Localization Master ⭐ 313

This is an unofficial implementation of the paper “PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization”.

Isolation Forest ⭐ 211

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

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

Novelty Detection ⭐ 183

Latent space autoregression for novelty detection.

Vae Lstm For Anomaly Detection ⭐ 128

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Unsupervised_anomaly_detection_brain_mri ⭐ 123

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

TOD: GPU-accelerated Outlier Detection via Tensor Operations

Student Teacher Anomaly Detection ⭐ 83

Student–Teacher Anomaly Detection with Discriminative Latent Embeddings

[CVPR 2023] Deep Feature In-painting for Unsupervised Anomaly Detection in X-ray Images

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.

Ods Anomalydetection ⭐ 50

Custom implementation of the DenStream algorithm in Python. The purpose is to detect anomalies applying the algorithm on Telemetry data coming from the devices.

Dcase2020_task2_baseline ⭐ 37

DCASE2020 Challenge Task 2 baseline system

An open-source unsupervised time-series anomaly detection package by Getcontact Data Team

Tsad Model Selection ⭐ 32

Code for "Unsupervised Model Selection for Time-series Anomaly Detection", ICLR 2023.

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.

Code for 'Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images' [MICCAI 2021]

Semiorthogonal ⭐ 19

Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

Kassandra ⭐ 11

Analysis of HTTP traffic and detection of anomalous user behavior in allowed actions. UEBA system.

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

Java Decision Forest ⭐ 9

Package implements decision tree and isolation forest

Dcase2021_task2_baseline_ae ⭐ 8

Autoencoder-based baseline system for DCASE2021 Challenge Task 2.

Anomaly Detection Libs ⭐ 8

Simple libraries for anomaly (outlier) detection

Real Time Ids ⭐ 8

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (Random forest) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (Auto encoders) for anomaly detection.

Anomaly Clustering ⭐ 8

An unofficial implementation using Pytorch for "Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types". Improve the algorithm with DINO pretrained ViT. Implement algorithms based on PatchCore.

Lgn Autoencoder ⭐ 7

Lorentz group equivariant autoencoders based on Lorentz Group Network

Latent Ad Qml ⭐ 6

Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning.

Padim Efficientnetv2 ⭐ 5

EfficientNetV2 based PaDiM

Code for paper entitled "Learning to detect RFI in radio astronomy without seeing it"

Dagmm Unsupervised Anomaly Detection ⭐ 5

Unsupervised Time Series Anomaly Detection

Reconstruction by Inpainting Based Anomaly Detection

Sparkstreaming Network Anomaly Detection ⭐ 5

This repository includes supervised and unsupervised machine learning methods which are used to detect anomalies on network datasets. Decision Tree, Random Forest, Gradient Boost Tree, Naive Bayes, and Logistic Regression were used for supervised learning. K-Means was used for unsupervised learning.

Related Searches

Python Unsupervised Learning (610)

Python Anomaly Detection (463)

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