About

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

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Subtasks

Datasets

Greatest papers with code

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

16 Sep 2020signals-dev/Orion

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

12 Feb 2018KDD-OpenSource/DeepADoTS

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

ICLR 2018 danieltan07/dagmm

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

DENSITY ESTIMATION DIMENSIONALITY REDUCTION UNSUPERVISED ANOMALY DETECTION

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

17 Mar 2017LeeDoYup/AnoGAN

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

UNSUPERVISED ANOMALY DETECTION

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

ICCV 2019 donggong1/memae-anomaly-detection

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

Re Learning Memory Guided Normality for Anomaly Detection

29 Jan 2021cvlab-yonsei/MNAD

The authors have introduced a novel method for unsupervised anomaly detection that utilises a newly introduced Memory Module in their paper.

UNSUPERVISED ANOMALY DETECTION

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

25 Jan 2019samet-akcay/skip-ganomaly

By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.

SCENE UNDERSTANDING UNSUPERVISED ANOMALY DETECTION

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

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

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

UNSUPERVISED ANOMALY DETECTION