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 401–425 of 506 papers

Title	Date	Tasks	Status
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities	Apr 30, 2021	Anomaly DetectionMulti-class Classification	—Unverified
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis	Apr 28, 2021	Anomaly DetectionFace Generation	—Unverified
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder	Apr 22, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Autoencoders for unsupervised anomaly detection in high energy physics	Apr 19, 2021	Anomaly DetectionJet Tagging	—Unverified
Concentration bounds for the empirical angular measure with statistical learning applications	Apr 7, 2021	Anomaly DetectionBinary Classification	CodeCode Available
Detecting Anomalies Through Contrast in Heterogeneous Data	Apr 2, 2021	Anomaly DetectionContrastive Learning	—Unverified
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI	Mar 24, 2021	Anomaly DetectionLesion Detection	—Unverified
Anomaly Detection using Principles of Human Perception	Mar 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Unsupervised anomaly detection in digital pathology using GANs	Mar 16, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection	Mar 16, 2021	Anomaly DetectionDensity Estimation	—Unverified
A new interpretable unsupervised anomaly detection method based on residual explanation	Mar 14, 2021	Anomaly DetectionDecision Making	—Unverified
Bump Hunting in Latent Space	Mar 11, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Unsupervised Brain Anomaly Detection and Segmentation with Transformers	Feb 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Leveraging 3D Information in Unsupervised Brain MRI Segmentation	Jan 26, 2021	Anomaly DetectionMRI segmentation	—Unverified
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators	Jan 12, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
Deep unsupervised anomaly detection	Jan 5, 2021	Anomaly DetectionClustering	—Unverified
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection	Jan 1, 2021	Anomaly DetectionImage Inpainting	—Unverified
Learning Deep Latent Variable Models via Amortized Langevin Dynamics	Jan 1, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Understanding Bias in Anomaly Detection: A Semi-Supervised View with PAC Guarantees	Jan 1, 2021	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
GenAD: General Representations of Multivariate Time Series for Anomaly Detection	Jan 1, 2021	Anomaly DetectionManagement	—Unverified
A General Framework for Unsupervised Anomaly Detection	Jan 1, 2021	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape	Dec 21, 2020	Anomaly DetectionBinary Classification	—Unverified
Anomaly detection through latent space restoration using vector-quantized variational autoencoders	Dec 12, 2020	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Unsupervised Anomaly Detection From Semantic Similarity Scores	Dec 1, 2020	Anomaly DetectionOut-of-Distribution Detection	—Unverified

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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