Unsupervised Anomaly Detection

67 papers with code • 9 benchmarks • 12 datasets

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.

Source: Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Greatest papers with code

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

signals-dev/Orion 16 Sep 2020

However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.

Time Series Unsupervised Anomaly Detection

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

KDD-OpenSource/DeepADoTS 12 Feb 2018

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.

Unsupervised Anomaly Detection

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

danieltan07/dagmm ICLR 2018

In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.

Density Estimation Dimensionality Reduction +1

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

donggong1/memae-anomaly-detection ICCV 2019

At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.

Unsupervised Anomaly Detection

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

LeeDoYup/AnoGAN 17 Mar 2017

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.

Unsupervised Anomaly Detection

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master 17 Nov 2020

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting.

Ranked #6 on Anomaly Detection on MVTec AD (using extra training data)

Unsupervised Anomaly Detection

[Re] Learning Memory Guided Normality for Anomaly Detection

cvlab-yonsei/MNAD RC 2020

We reused the available code to build scripts for the Reconstruction task and variants with and without memory.

Hyperparameter Optimization Unsupervised Anomaly Detection

MVTec AD -- A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

AdneneBoumessouer/MVTec-Anomaly-Detection CVPR 2019

To the best of our knowledge, this is the first comprehensive, multi-object, multi-defect dataset for anomaly detection that provides pixel-accurate ground truth regions and focuses on real-world applications.

Ranked #25 on Anomaly Detection on MVTec AD (Segmentation AUROC metric)

Unsupervised Anomaly Detection