Unsupervised Anomaly Detection

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 476–500 of 506 papers

Title	Date	Tasks	Status
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection	Mar 3, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"	Feb 10, 2019	BenchmarkingClustering	—Unverified
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks	Jan 30, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection	Jan 25, 2019	Anomaly DetectionDecoder	CodeCode Available
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention	Dec 17, 2018	Anomaly DetectionDeep Attention	—Unverified
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection	Dec 14, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation	Dec 14, 2018	Anomaly DetectionDensity Estimation	—Unverified
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data	Nov 20, 2018	Anomaly DetectionDecoder	CodeCode Available
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma	Oct 22, 2018	Anomaly DetectionLEMMA	CodeCode Available
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN	Aug 23, 2018	Anomaly DetectionData Augmentation	—Unverified
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images	Aug 10, 2018	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data	Jul 3, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Deep Active Learning for Anomaly Detection	May 23, 2018	Active LearningAnomaly Detection	—Unverified
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic	May 9, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Incorporating Privileged Information to Unsupervised Anomaly Detection	May 6, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features	Apr 13, 2018	Anomaly DetectionDecision Making	CodeCode Available
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images	Apr 12, 2018	AnatomyAnomaly Detection	CodeCode Available
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations	Feb 1, 2018	Anomaly DetectionClustering	—Unverified
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure	Jan 24, 2018	Anomaly DetectionClustering	—Unverified
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Jan 1, 2018	Anomaly DetectionDensity Estimation	CodeCode Available
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things	Dec 18, 2017	Anomaly DetectionEdge-computing	—Unverified
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups	Jun 20, 2017	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery	Mar 17, 2017	Anomaly DetectionGenerative Adversarial Network	CodeCode Available
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies	Feb 16, 2017	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified

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All datasets AnoShift SMAP Vehicle Claims Fashion-MNIST KolektorSDD2 20NEWS AeBAD-S Caltech-101 DAGM2007 ECG5000 KolektorSDD MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ACR-NTL (zero-shot, test anomaly ratio=1%)	ROC-AUC FAR	62.5	—	Unverified
2	ACR-DSVDD (zero-shot, anomaly ratio=1%)	ROC-AUC FAR	62	—	Unverified
3	ACR-NTL (zero-shot, test anomaly ratio=20%)	ROC-AUC FAR	62	—	Unverified
4	ACR-DSVDD (zero-shot, anomaly ratio=20%)	ROC-AUC FAR	59.1	—	Unverified
5	COPOD	ROC-AUC FAR	50.42	—	Unverified
6	OC-SVM	ROC-AUC FAR	49.57	—	Unverified
7	SO-GAAL	ROC-AUC FAR	49.35	—	Unverified
8	ECOD Li et al. (2022)	ROC-AUC FAR	49.19	—	Unverified
9	LOF	ROC-AUC FAR	34.96	—	Unverified
10	deepSVDD	ROC-AUC FAR	34.53	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DFM (flow matching)	F1	94.1	—	Unverified
2	ContextFlow++ (Glow-based)	F1	93.62	—	Unverified
3	TranAd	F1	89.15	—	Unverified
4	MTAD-GAT	F1	88.8	—	Unverified
5	CAE-M	F1	88.27	—	Unverified
6	OmniAnomaly	F1	87.28	—	Unverified
7	Glow	F1	86.05	—	Unverified
8	GDN	F1	85.18	—	Unverified
9	USAD	F1	81.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SOM	AUC	65.43	—	Unverified
2	Isolation Forest	AUC	59.42	—	Unverified
3	Latent Outlier Exposure	AUC	58.59	—	Unverified
4	NeuTraL-AD	AUC	57.03	—	Unverified
5	RSRAE	AUC	55.38	—	Unverified
6	SOM-DAGMM	AUC	53.82	—	Unverified
7	Local Outlier Factor	AUC	52.86	—	Unverified
8	One Class Support Vector Machines	AUC	51.68	—	Unverified
9	DAGMM	AUC	51.22	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RSRAE	AUC-ROC	0.85	—	Unverified
2	RSRAE	AUC (outlier ratio = 0.5)	0.83	—	Unverified
3	RSRAE	AUC-ROC	0.75	—	Unverified
4	RSRAE	AUC-ROC	0.69	—	Unverified
5	RSRAE	AUC-ROC	0.69	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Semi-orthogonal	Segmentation AUROC	98.1	—	Unverified
2	WeakREST-Un	Segmentation AP	76.9	—	Unverified
3	DSR	Segmentation AP	61.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RSRAE	AUC (outlier ratio = 0.5)	0.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSFR	Detection AUROC	87.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RSRAE	AUC (outlier ratio = 0.5)	0.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DiffusionAD	Detection AUROC	99.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VRAE+SVM	AUC	0.98	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Semi-orthogonal	Segmentation AUROC	96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LVAD	AUROC	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DyEdgeGAT	AUC	0.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RSRAE	AUC (outlier ratio = 0.5)	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TranAD	Precision	92.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LVAD	AUC-ROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DyEdgeGAT	AUC	0.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	P-CAE W-MSE (Tilted View)	AUROC	78.1	—	Unverified