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 126–150 of 506 papers

Title	Date	Tasks	Status	Hype
Label-Free Multivariate Time Series Anomaly Detection	Dec 17, 2023	Anomaly DetectionDensity Estimation	CodeCode Available	1
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization	Nov 17, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder	Apr 7, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1
Deep Anomaly Detection on Tennessee Eastman Process Data	Mar 10, 2023	Anomaly DetectionChemical Process	—Unverified	0
Deep Anomaly Detection in Text	Dec 14, 2023	Anomaly DetectionRepresentation Learning	—Unverified	0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified	0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection	Jun 2, 2021	Anomaly DetectionData Augmentation	—Unverified	0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts	Nov 26, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling	May 28, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection	Apr 14, 2023	Anomaly DetectionDensity Estimation	—Unverified	0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization	Jan 5, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
An optimization method for out-of-distribution anomaly detection models	Feb 2, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection	Oct 26, 2022	Anomaly DetectionAnomaly Segmentation	—Unverified	0
A2Log: Attentive Augmented Log Anomaly Detection	Sep 20, 2021	Anomaly DetectionData Augmentation	—Unverified	0
AI for human assessment: What do professional assessors need?	Apr 18, 2022	Anomaly DetectionDecision Making	—Unverified	0
Anomaly Detection with Inexact Labels	Sep 11, 2019	Anomaly DetectionMultiple Instance Learning	—Unverified	0
Fairness-aware Anomaly Detection via Fair Projection	May 16, 2025	Anomaly DetectionFairness	—Unverified	0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model	May 15, 2025	Anomaly DetectionLanguage Modeling	—Unverified	0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection	Dec 2, 2022	Anomaly DetectionDeep Learning	—Unverified	0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection	Dec 14, 2018	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Constructing a meta-learner for unsupervised anomaly detection	Apr 22, 2023	Anomaly DetectionAutoML	—Unverified	0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers	Jun 6, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"	Feb 10, 2019	BenchmarkingClustering	—Unverified	0
Anomaly Detection with Adversarially Learned Perturbations of Latent Space	Jul 3, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0

Show:10 25 50

← PrevPage 6 of 21Next →

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