ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

Codebase for Patched Diffusion Models for Unsupervised Anomaly Detection .

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.

This is an unofficial implementation of ' Anomaly localization by modeling perceptual features'

Codebase for Conditioned Diffusion Models for Unsupervised Anomaly Detection

MICCAI 2021 | Adversarial based selective network for unsupervised anomaly segmentation

A Collection of Data sets and Approaches to UAD in Brain MRI.

Codebase for Unsupervised Anomaly Detection using Aggregated Normative Diffusion (ANDi)

The source code of Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection (RAND), ICDM 2023.

반도체 소자 이상 탐지 AI 경진대회, DACON (2024.02.05 ~ 2024.03.04)

Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly Detection

An implementation of the DeepAnT model, a deep learning approach for unsupervised anomaly detection in time series data.

Unsupervised Anomaly Detection Utilizing a Teacher-Student Model Enhanced by Generative Adversarial Networks.

This research project intent is to review and demonstrate a comparability among recent auto-encoder methods by utilizing single architecture and resolution. Each method will be ranked based on selective performance measure in modeling healthy brain and the sensitivity towards domain shift.

Repo and code of the UbiComp-ISWC 2021 paper: 'Air Quality Sensor Network Data Acquisition, Cleaning, Visualization, and Analytics: A Real-world IoT Use Case'

In this research work, unsupervised abnormality has been detected by using intelligent and heterogeneous autonomous systems.

Unsupervised anomaly detection for time series using the autoencoder and process mining