anomaly-detection-algorithm

[ICLR 2026] The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

Semi-supervised anomaly detection method

[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

Detects anomalous resting heart rate from smartwatch data.

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

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

Artificial Immune Systems Package (AISP) is an open-source Python library that features bio-inspired algorithms based on artificial immune systems for machine learning, pattern recognition, anomaly detection, and optimization tasks.

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.

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

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

DecompositionUMAP: A multi-scale framework for pattern analysis and anomoly detection

Kernel-Level Energy Profiling & Anomaly Detection for Android

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

Benchmarking anomaly detection on air-quality time series using a tiny autoencoder and classical methods. Includes reproducible pipeline, strict time-aware evaluation, and analysis of thresholding strategies and model performance gaps.

Natural-language Q&A and anomaly detection over support tickets — FastAPI + SQL-grounded LLM, zero-cost, single command to run

UPIQAL is a 100% automated Full-Reference Image Quality Assessment (FR-IQA) framework. It synthesizes deep feature statistics, probabilistic uncertainty, and spatial heuristics to replace human MOS and output spatially localized diagnostic heatmaps for visual artifacts.

an end to end anomaly intrusion base on deep learn