Unsupervised Anomaly Detection

102 papers with code • 9 benchmarks • 13 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


Use these libraries to find Unsupervised Anomaly Detection models and implementations

Most implemented papers

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.

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

openvinotoolkit/anomalib 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.

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

korepwx/donut 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.

Student-Teacher Feature Pyramid Matching for Anomaly Detection

openvinotoolkit/anomalib 7 Mar 2021

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

ZKSI/CumFSel.jl 10 Aug 2018

In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

KONI-SZ/MSCRED 20 Nov 2018

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

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.

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

byungjae89/SPADE-pytorch 5 May 2020

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images.

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.

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

swlee23/deep-learning-time-series-anomaly-detection 19 Dec 2018

In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.