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 76–100 of 506 papers

Title	Date	Tasks	Status	Hype	Score
Iterative energy-based projection on a normal data manifold for anomaly localization	Feb 10, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
A SAM-guided Two-stream Lightweight Model for Anomaly Detection	Feb 29, 2024	Anomaly DetectionDecoder	CodeCode Available	1	5
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation	Jun 27, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data	Dec 25, 2021	Anomaly Detectioncontinual anomaly detection	CodeCode Available	1	5
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	Jul 12, 2020	Anomaly DetectionAttribute	CodeCode Available	1	5
Combining GANs and AutoEncoders for Efficient Anomaly Detection	Nov 16, 2020	Adversarial AttackAnomaly Detection	CodeCode Available	1	5
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs	May 9, 2022	AnatomyAnomaly Detection	CodeCode Available	1	5
DSR -- A dual subspace re-projection network for surface anomaly detection	Aug 2, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images	Jul 14, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	Aug 17, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5
MemStream: Memory-Based Streaming Anomaly Detection	Jun 7, 2021	Anomaly DetectionDenoising	CodeCode Available	1	5
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images	Sep 3, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems	Jul 7, 2023	Anomaly DetectionFault Detection	CodeCode Available	1	5
Multivariate Time-series Anomaly Detection via Graph Attention Network	Sep 4, 2020	Anomaly DetectionGraph Attention	CodeCode Available	1	5
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series	Apr 23, 2020	Anomaly DetectionRespiratory Failure	CodeCode Available	1	5
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection	Mar 18, 2024	Anomaly DetectionDenoising	CodeCode Available	1	5
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection	Oct 19, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization	Nov 17, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI	Mar 7, 2023	AnatomyAnomaly Detection	CodeCode Available	1	5
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1	5
FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model	Aug 31, 2024	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1	5
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows	Nov 15, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization	Jun 9, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows	Jul 27, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5
Clustered Hierarchical Anomaly and Outlier Detection Algorithms	Feb 9, 2021	Anomaly DetectionClustering	CodeCode Available	1	5

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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