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 426–450 of 506 papers

Title	Date	Tasks	Status
Time series anomaly detection with reconstruction-based state-space models	Mar 6, 2023	Anomaly DetectionDecoder	CodeCode Available
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection	Jul 17, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Less-supervised learning with knowledge distillation for sperm morphology analysis	May 8, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder	Mar 22, 2022	Anomaly DetectionImage Reconstruction	CodeCode Available
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios	Nov 30, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection	May 10, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection	Nov 26, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Learning Neural Representations for Network Anomaly Detection	Aug 1, 2019	Anomaly DetectionIntrusion Detection	CodeCode Available
Position Regression for Unsupervised Anomaly Detection	Jan 19, 2023	Anomaly DetectionImage Reconstruction	CodeCode Available
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection	Jan 24, 2025	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
Towards frugal unsupervised detection of subtle abnormalities in medical imaging	Sep 4, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Towards Phytoplankton Parasite Detection Using Autoencoders	Mar 15, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
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
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset	Nov 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection	Apr 4, 2025	Anomaly DetectionAnomaly Localization	CodeCode Available
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders	Apr 17, 2025	Anomaly DetectionQuantum Machine Learning	CodeCode Available
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty	Jun 28, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Distillation-based fabric anomaly detection	Jan 4, 2024	Anomaly DetectionDefect Detection	CodeCode Available
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection	Mar 21, 2024	Anomaly DetectionMedical Image Analysis	CodeCode Available
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks	Oct 2, 2024	Anomaly DetectionGraph Learning	CodeCode Available
Anomaly detection with superexperts under delayed feedback	Oct 8, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Deep Weakly-supervised Anomaly Detection	Oct 30, 2019	Anomaly DetectionRelation	CodeCode Available
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset	Apr 17, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?	Jul 5, 2016	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection	Apr 2, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available

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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