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 101–125 of 506 papers

Title	Date	Tasks	Status	Hype
Student-Teacher Feature Pyramid Matching for Anomaly Detection	Mar 7, 2021	Anomaly Detectionimage-classification	CodeCode Available	1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images	Mar 5, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
Clustered Hierarchical Anomaly and Outlier Detection Algorithms	Feb 9, 2021	Anomaly DetectionClustering	CodeCode Available	1
[Re] Learning Memory Guided Normality for Anomaly Detection	Jan 29, 2021	Anomaly DetectionHyperparameter Optimization	CodeCode Available	1
Deep One-Class Classification via Interpolated Gaussian Descriptor	Jan 25, 2021	Anomaly DetectionClassification	CodeCode Available	1
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders	Jan 1, 2021	Anomaly DetectionMissing Values	CodeCode Available	1
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization	Nov 17, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Combining GANs and AutoEncoders for Efficient Anomaly Detection	Nov 16, 2020	Adversarial AttackAnomaly Detection	CodeCode Available	1
Multivariate Time-series Anomaly Detection via Graph Attention Network	Sep 4, 2020	Anomaly DetectionGraph Attention	CodeCode Available	1
USAD: UnSupervised Anomaly Detection on Multivariate Time Series	Aug 23, 2020	Anomaly DetectionTime Series	CodeCode Available	1
A General Framework For Detecting Anomalous Inputs to DNN Classifiers	Jul 29, 2020	Anomaly Detectionimage-classification	CodeCode Available	1
Fast Unsupervised Anomaly Detection in Traffic Videos	Jul 28, 2020	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance	Jul 21, 2020	Anomaly DetectionFeature Importance	CodeCode Available	1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances	Jul 16, 2020	Anomaly DetectionContrastive Learning	CodeCode Available	1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	Jul 12, 2020	Anomaly DetectionAttribute	CodeCode Available	1
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders	Jun 23, 2020	Anomaly DetectionMedical Image Analysis	CodeCode Available	1
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds	Jun 18, 2020	Anomaly DetectionTask 2	CodeCode Available	1
Probabilistic Autoencoder	Jun 9, 2020	Bayesian InferenceDenoising	CodeCode Available	1
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity	May 29, 2020	Active LearningAnomaly Detection	CodeCode Available	1
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization	May 12, 2020	Anomaly DetectionMetric Learning	CodeCode Available	1
Sub-Image Anomaly Detection with Deep Pyramid Correspondences	May 5, 2020	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection	May 5, 2020	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series	Apr 23, 2020	Anomaly DetectionRespiratory Failure	CodeCode Available	1
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study	Apr 7, 2020	AnatomyAnomaly Detection	CodeCode Available	1
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders	Mar 24, 2020	Anomaly DetectionOut of Distribution (OOD) Detection	CodeCode Available	1

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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