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 201–225 of 506 papers

Title	Date	Tasks	Status	Score
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection	Mar 21, 2024	Anomaly DetectionMedical Image Analysis	CodeCode Available	5
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection	May 16, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	5
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection	May 10, 2024	Anomaly DetectionKnowledge Distillation	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
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding	Apr 21, 2025	Anomaly DetectionTime Series	CodeCode Available	5
Towards frugal unsupervised detection of subtle abnormalities in medical imaging	Sep 4, 2023	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
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series	Nov 21, 2024	Anomaly DetectionBenchmarking	CodeCode Available	5
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection	Jul 17, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series	Aug 30, 2022	Anomaly DetectionDecoder	CodeCode Available	5
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series	Dec 21, 2022	Anomaly DetectionTime Series	CodeCode Available	5
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI	Nov 28, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset	Nov 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
Learning Neural Representations for Network Anomaly Detection	Aug 1, 2019	Anomaly DetectionIntrusion Detection	CodeCode Available	5
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?	Jul 5, 2016	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	5
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images	Apr 12, 2018	AnatomyAnomaly Detection	CodeCode Available	5
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset	Apr 17, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection	Jan 30, 2025	Anomaly DetectionTime Series	CodeCode Available	5
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Jan 1, 2018	Anomaly DetectionDensity Estimation	CodeCode Available	5
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available	5
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies	Aug 2, 2023	Anomaly DetectionImage Segmentation	CodeCode Available	5
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection	Apr 2, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	5
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks	Oct 2, 2024	Anomaly DetectionGraph Learning	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
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging	Nov 6, 2024	Anomaly Detectioncounterfactual	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