168 Open source projects that are alternatives of or similar to deviation-network-image

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

TOD: GPU-accelerated Outlier Detection via Tensor Operations

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

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

A scikit-learn compatible library for anomaly detection

ADRepository: Real-world anomaly detection datasets

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

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

Main component extraction for outlier detection

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

Anomaly detection related books, papers, videos, and toolboxes

Code for reproducing f-AnoGAN in Pytorch

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

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

some anomaly detection models and experiments.

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

A parallel implementation of local outlier factor based on Spark

No description or website provided.

Python toolbox for transfer learning.

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

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

A Python library for anomaly detection

Anomaly detection library based on singular spectrum transformation(sst)

Unofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)

Sherlock is an anomaly detection service built on top of Druid

Interactive Visualization to Build, Train and Test an Autoencoder with Tensorflow.js

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

Pytorch Implementation of DeepLog.

This is the code for the paper nemed 'Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation'

Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo.

DDC-Outlier: Preventing medication errors using unsupervised learning

Anomaly detection for temporal data using LSTMs

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

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Open-source framework to detect outliers in Elasticsearch events

Log Anomaly Detection - Machine learning to detect abnormal events logs

A Ground Mobile Robot Perception Dataset, IEEE RA-L & IEEE T-CYB

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

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…

The fundamental package for AIops with python.

The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks

Hastic data management server for labeling patterns and anomalies in Grafana

Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Repository for the Deep One-Class Classification ICML 2018 paper

No description or website provided.

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Isolation Forest on Spark

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Fairness regulator and rate limiter

Anomaly detection for streaming time series, featuring automated model selection.

Unsupervised anomaly detection with generative model, keras implementation

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)

Latent space autoregression for novelty detection.

基于智能计算框架nupic的异常检测restful Api.

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

anomaly detection by one-class SVM

The Kubervisor allow you to control which pods should receive traffic or not based on anomaly detection.It is a new kind of health check system.

Arduino library for identifying outliers with a Hampel filter