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 151–175 of 506 papers

Title	Date	Tasks	Status	Score
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features	Apr 13, 2018	Anomaly DetectionDecision Making	CodeCode Available	5
Anomaly detection with superexperts under delayed feedback	Oct 8, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Anomaly Detection using Principles of Human Perception	Mar 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection	Apr 4, 2025	Anomaly DetectionAnomaly Localization	CodeCode Available	5
Position Regression for Unsupervised Anomaly Detection	Jan 19, 2023	Anomaly DetectionImage Reconstruction	CodeCode Available	5
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders	Apr 17, 2025	Anomaly DetectionQuantum Machine Learning	CodeCode Available	5
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection	Mar 18, 2025	Anomaly DetectionKnowledge Distillation	CodeCode Available	5
Bump Hunting in Latent Space	Mar 11, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination	Oct 28, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available	5
Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice	Sep 13, 2024	Anomaly DetectionBinary Classification	CodeCode Available	5
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol	Jun 30, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability	Apr 11, 2024	Anomaly DetectionLanguage Modelling	CodeCode Available	5
Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplification	Aug 28, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection	May 16, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	5
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images	Aug 10, 2018	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Anomaly Detection by Recombining Gated Unsupervised Experts	Aug 31, 2020	Anomaly DetectionMixture-of-Experts	CodeCode Available	5
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection	Jul 13, 2023	Anomaly DetectionData Augmentation	CodeCode Available	5
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Less-supervised learning with knowledge distillation for sperm morphology analysis	May 8, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available	5
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection	Jul 17, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators	Jan 12, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector	Oct 30, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	Dec 1, 2019	Outlier DetectionRepresentation Learning	CodeCode Available	5
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding	Apr 21, 2025	Anomaly DetectionTime Series	CodeCode Available	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