Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 921–930 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Sets	Mar 10, 2017	Anomaly DetectionOutlier Detection	CodeCode Available	1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	Oct 7, 2016	Anomaly DetectionAutomatic Speech Recognition	CodeCode Available	1
Robust random cut forest based anomaly detection on streams	Jun 19, 2016	Anomaly Detection	CodeCode Available	1
Deep Structured Energy Based Models for Anomaly Detection	May 25, 2016	Anomaly Detection	CodeCode Available	1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark	Oct 12, 2015	Anomaly DetectionTime Series	CodeCode Available	1
Auto-Encoding Variational Bayes	Dec 20, 2013	Anomaly DetectionImage Clustering	CodeCode Available	1
Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems	Jul 21, 2025	Anomaly Detection	—Unverified	0
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys	Jul 17, 2025	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering	Jul 17, 2025	Anomaly DetectionAnomaly Localization	—Unverified	0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy	Jul 16, 2025	Anomaly DetectionFederated Learning	—Unverified	0

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified