[CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".

Anomaly detection method that incorporates multi-scale features to sparse coding

Semi-supervised anomaly detection method

Detects anomalous resting heart rate from smartwatch data.

This project provides a time series anomaly detection algorithm based on the dynamic threshold generation model.

Several examples of anomaly detection algorithms for time series data.

An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.

Uses LSTM-based autoencoders to detect abnormal resting heart rate during the coronavirus (SARS-CoV-2) infectious period using the wearables data.

The paper "Deep Graph Level Anomaly Detection with Contrastive Learning" has been accepted by Scientific Reports Journal.

OCR to detect and recognize dot-matrix text written with inkjet-printed on medical PVC bag

Anomaly detection algorithm for time series based on the dynamic threshold generation model

an end to end anomaly intrusion base on deep learn