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 51–75 of 506 papers

Title	Date	Tasks	Status	Hype
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
Unsupervised anomaly detection algorithms on real-world data: how many do we need?	May 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder	Apr 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach	Mar 31, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks	Mar 22, 2023	Anomaly DetectionAutonomous Driving	CodeCode Available	1
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI	Mar 7, 2023	AnatomyAnomaly Detection	CodeCode Available	1
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline	Mar 1, 2023	Anomaly DetectionDomain Generalization	CodeCode Available	1
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art	Mar 1, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Zero-Shot Anomaly Detection via Batch Normalization	Feb 15, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection	Jan 27, 2023	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	1
The role of noise in denoising models for anomaly detection in medical images	Jan 19, 2023	Anomaly DetectionDenoising	CodeCode Available	1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG	Jan 3, 2023	Anomaly DetectionEEG	CodeCode Available	1
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification	Jan 1, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection	Jan 1, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available	1
Anomaly Detection using Score-based Perturbation Resilience	Jan 1, 2023	Anomaly DetectionDenoising	CodeCode Available	1
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings	Oct 25, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection	Oct 19, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization	Oct 10, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease	Sep 5, 2022	Anomaly DetectionDiagnostic	CodeCode Available	1
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
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

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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