173 Open source projects that are alternatives of or similar to Luminol

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

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A curated list of awesome anomaly detection resources

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iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

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RNN based Time-series Anomaly detector model implemented in Pytorch.

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This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Anomaly detection related books, papers, videos, and toolboxes

Merlion: A Machine Learning Framework for Time Series Intelligence

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

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This repository is the PyTorch implementation of GAN Ensemble for Anomaly Detection.

Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

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使用极端值理论(Extreme Value Theory)实现阈值动态自动化设置

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Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

ADRepository: Real-world anomaly detection datasets

Official implementation of Paper Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018

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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

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