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 251–275 of 506 papers

Title	Date	Tasks	Status	Hype
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!	Oct 6, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection	Sep 26, 2022	Anomaly DetectionSegmentation	—Unverified	0
Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds	Sep 23, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis	Sep 10, 2022	Anomaly DetectionDiversity	—Unverified	0
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease	Sep 5, 2022	Anomaly DetectionDiagnostic	CodeCode Available	1
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series	Aug 30, 2022	Anomaly DetectionDecoder	CodeCode Available	0
RUAD: unsupervised anomaly detection in HPC systems	Aug 28, 2022	Anomaly DetectionClustering	—Unverified	0
Unsupervised Anomaly Localization with Structural Feature-Autoencoders	Aug 23, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes	Aug 17, 2022	Anomaly DetectionChemical Process	CodeCode Available	1
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT	Aug 17, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success	Aug 16, 2022	Anomaly DetectionData Augmentation	CodeCode Available	0
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization	Aug 6, 2022	Anomaly DetectionSegmentation	—Unverified	0
Detecting Multivariate Time Series Anomalies with Zero Known Label	Aug 3, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	1
DSR -- A dual subspace re-projection network for surface anomaly detection	Aug 2, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Scrutinizing Shipment Records To Thwart Illegal Timber Trade	Jul 31, 2022	Anomaly DetectionContrastive Learning	—Unverified	0
A general-purpose method for applying Explainable AI for Anomaly Detection	Jul 23, 2022	Anomaly DetectionExplainable Artificial Intelligence (XAI)	—Unverified	0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks	Jul 20, 2022	Anomaly DetectionAnomaly Localization	—Unverified	0
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images	Jul 5, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space	Jul 3, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise	Jun 30, 2022	Anomaly DetectionDenoising	CodeCode Available	2
Learning Citywide Patterns of Life from Trajectory Monitoring	Jun 30, 2022	Anomaly DetectionAutonomous Vehicles	—Unverified	0
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection	Jun 30, 2022	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation	Jun 27, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching	Jun 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography	Jun 18, 2022	Anomaly DetectionComputed Tomography (CT)	CodeCode Available	0

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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