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 176–200 of 506 papers

Title	Date	Tasks	Status	Hype
Patch-wise Auto-Encoder for Visual Anomaly Detection	Aug 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays	Jul 24, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models	Jul 23, 2023	Anomaly DetectionDensity Estimation	—Unverified	0
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection	Jul 16, 2023	Anomaly DetectionMulti-class Anomaly Detection	—Unverified	0
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images	Jul 14, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection	Jul 13, 2023	Active LearningAnomaly Detection	—Unverified	0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection	Jul 13, 2023	Anomaly DetectionData Augmentation	CodeCode Available	0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification	Jul 12, 2023	Anomaly DetectionPhotoplethysmography (PPG)	—Unverified	0
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation	Jul 11, 2023	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems	Jul 7, 2023	Anomaly DetectionFault Detection	CodeCode Available	1
Exploring Dual Model Knowledge Distillation for Anomaly Detection	Jun 27, 2023	Anomaly Detectionfeature selection	—Unverified	0
Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach	Jun 19, 2023	Anomaly DetectionStructural Health Monitoring	—Unverified	0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning	Jun 15, 2023	Anomaly DetectionNovelty Detection	—Unverified	0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation	Jun 7, 2023	Anomaly DetectionExplainable artificial intelligence	—Unverified	0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers	Jun 6, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction	Jun 5, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
UADB: Unsupervised Anomaly Detection Booster	Jun 3, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model	May 31, 2023	AnatomyAnomaly Detection	CodeCode Available	1
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies	May 30, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling	May 28, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision	May 25, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Multiresolution Feature Guidance Based Transformer for Anomaly Detection	May 24, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection	May 22, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection	May 21, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Unsupervised anomaly detection algorithms on real-world data: how many do we need?	May 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1

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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