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 451–475 of 506 papers

Title	Date	Tasks	Status
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series	Aug 30, 2022	Anomaly DetectionDecoder	CodeCode Available
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images	Apr 12, 2018	AnatomyAnomaly Detection	CodeCode Available
Anomaly Detection via Self-organizing Map	Jul 21, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images	Jul 5, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available
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
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network	Jul 25, 2019	Anomaly DetectionTime Series	CodeCode Available
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection	Mar 30, 2019	Anomaly DetectionDecoder	CodeCode Available
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success	Aug 16, 2022	Anomaly DetectionData Augmentation	CodeCode Available
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Jan 1, 2018	Anomaly DetectionDensity Estimation	CodeCode Available
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM	Nov 21, 2019	Anomaly DetectionDiversity	CodeCode Available
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord	Mar 17, 2025	Anomaly DetectionLesion Detection	CodeCode Available
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features	Apr 13, 2018	Anomaly DetectionDecision Making	CodeCode Available
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection	Mar 18, 2025	Anomaly DetectionKnowledge Distillation	CodeCode Available
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol	Jun 30, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations	Oct 4, 2024	Anomaly DetectionTest-time Adaptation	CodeCode Available
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection	Mar 3, 2019	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text	Jul 1, 2019	Anomaly DetectionContextual Anomaly Detection	CodeCode Available
Anomaly Detection by Recombining Gated Unsupervised Experts	Aug 31, 2020	Anomaly DetectionMixture-of-Experts	CodeCode Available
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus	Apr 29, 2024	Anomaly DetectionSelf-Supervised Learning	CodeCode Available
Anomaly Detection using Principles of Human Perception	Mar 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees	Jan 1, 2021	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings	Nov 6, 2019	Anomaly DetectionAnomaly Segmentation	CodeCode Available
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding	Jun 2, 2024	Anomaly DetectionDensity Estimation	CodeCode Available
Unsupervised crack detection on complex stone masonry surfaces	Mar 31, 2023	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